diff --git a/navis/sampling/resampling.py b/navis/sampling/resampling.py
index 63c5922e..a2c9d715 100644
--- a/navis/sampling/resampling.py
+++ b/navis/sampling/resampling.py
@@ -65,13 +65,17 @@ def resample_neuron(x: 'core.NeuronObject',
                     ) -> Optional['core.NeuronObject']:
     """Resample neuron(s) to given resolution.
 
-    Preserves root, leafs, branchpoints. Connectors (if they exist) are mapped
-    onto the closest new node.
+    Preserves root, leafs, branchpoints. Soma, connectors and node tags
+    (if present) are mapped onto the closest node in the resampled neuron.
 
     Important
     ---------
-    This generates an entirely new set of node IDs! Those will be unique
-    within a neuron, but you may encounter duplicates across neurons.
+    A few things to keep in mind:
+      - This generates an entirely new set of node IDs! Those will be unique
+        within a neuron, but you may encounter duplicates across neurons.
+      - Any non-standard node table columns (e.g. "labels") will be lost.
+      - Soma(s) will be pinned to the closest node in the resampled neuron.
+
 
     Also: be aware that high-resolution neurons will use A LOT of memory.
 
@@ -128,15 +132,17 @@ def resample_neuron(x: 'core.NeuronObject',
     if isinstance(x, core.NeuronList):
         if not inplace:
             x = x.copy()
-        results = [resample_neuron(x[i], resample_to,
-                                   method=method, inplace=True,
-                                   skip_errors=skip_errors)
-                   for i in config.trange(x.shape[0],
-                                          desc='Resampl. neurons',
-                                          disable=config.pbar_hide,
-                                          leave=config.pbar_leave)]
+        _ = [resample_neuron(x[i],
+                             resample_to=resample_to,
+                             method=method, inplace=True,
+                             skip_errors=skip_errors)
+             for i in config.trange(x.shape[0],
+                                    desc='Resampl. neurons',
+                                    disable=config.pbar_hide,
+                                    leave=config.pbar_leave)]
+
         if not inplace:
-            return core.NeuronList(results)
+            return x
         return None
     elif not isinstance(x, core.TreeNeuron):
         raise TypeError(f'Unable to resample data of type "{type(x)}"')
@@ -237,32 +243,72 @@ def resample_neuron(x: 'core.NeuronObject',
     # Generate new nodes dataframe
     new_nodes = pd.DataFrame(data=new_nodes,
                              columns=['node_id', 'parent_id',
-                                      'x', 'y', 'z', 'radius'],
-                             dtype=object
-                             )
+                                      'x', 'y', 'z', 'radius'])
 
     # Convert columns to appropriate dtypes
-    dtypes = {'node_id': int, 'parent_id': int, 'x': float, 'y': float,
-              'z': float, 'radius': float}
+    dtypes = {k: x.nodes[k].dtype for k in ['node_id', 'parent_id', 'x', 'y', 'z', 'radius']}
 
-    for k, v in dtypes.items():
-        new_nodes[k] = new_nodes[k].astype(v)
+    for cols in new_nodes.columns:
+        new_nodes = new_nodes.astype(dtypes, errors='ignore')
 
     # Remove duplicate nodes (branch points)
     new_nodes = new_nodes[~new_nodes.node_id.duplicated()]
 
+    # Generate KDTree
+    tree = scipy.spatial.cKDTree(new_nodes[['x', 'y', 'z']].values)
+    # Map soma onto new nodes if required
+    # Note that if `._soma` is a soma detection function we can't tell
+    # how to deal with it. Ideally the new soma node will
+    # be automatically detected but it is possible, for example, that
+    # the radii of nodes have changed due to interpolation such that more
+    # than one soma is detected now. Also a "label" column in the node
+    # table would be lost at this point.
+    # We will go for the easy option which is to pin the soma at this point.
+    if np.any(getattr(x, 'soma')):
+        soma_nodes = utils.make_iterable(x.soma)
+        old_pos = nodes.loc[soma_nodes, ['x', 'y', 'z']].values
+
+        # Get nearest neighbours
+        dist, ix = tree.query(old_pos)
+        node_map = dict(zip(soma_nodes, new_nodes.node_id.values[ix]))
+
+        # Map back onto neuron
+        if utils.is_iterable(x.soma):
+            x.soma = [node_map[n] for n in x.soma]
+        else:
+            x.soma = node_map[x.soma]
+    else:
+        # If `._soma` was (read: is) a function but it didn't detect anything in
+        # the original neurons, this makes sure that the resampled neuron
+        # doesn't have a soma either:
+        x.soma = None
+
+    # Map connectors back if necessary
     if x.has_connectors:
-        # Map connectors back:
-        # 1. Get position of old synapse-bearing nodes
-        old_tn_position = x.nodes.set_index('node_id',
-                                            inplace=False).loc[x.connectors.node_id,
-                                                               ['x', 'y', 'z']].values
-        # 2. Get closest neighbours
-        distances = scipy.spatial.distance.cdist(old_tn_position,
-                                                 new_nodes[['x', 'y', 'z']].values)
-        min_ix = np.argmin(distances, axis=1)
-        # 3. Map back onto neuron
-        x.connectors['node_id'] = new_nodes.iloc[min_ix].node_id.values
+        # Get position of old synapse-bearing nodes
+        old_tn_position = nodes.loc[x.connectors.node_id, ['x', 'y', 'z']].values
+
+        # Get nearest neighbours
+        dist, ix = tree.query(old_tn_position)
+
+        # Map back onto neuron
+        x.connectors['node_id'] = new_nodes.node_id.values[ix]
+
+    # Map tags back if necessary
+    # Expects `tags` to be a dictionary {'tag': [node_id1, node_id2, ...]}
+    if x.has_tags and isinstance(x.tags, dict):
+        # Get nodes that need remapping
+        nodes_to_remap = {n for l in x.tags.values() for n in l}
+
+        # Get position of old tag-bearing nodes
+        old_tn_position = nodes.loc[nodes_to_remap, ['x', 'y', 'z']].values
+
+        # Get nearest neighbours
+        dist, ix = tree.query(old_tn_position)
+
+        # Map back onto tags
+        node_map = dict(zip(nodes_to_remap, new_nodes.node_id.values[ix]))
+        x.tags = {k: [node_map[n] for n in v] for k, v in x.tags.items()}
 
     # Set nodes
     x.nodes = new_nodes