diff --git a/doc/examples/plot_ncut.py b/doc/examples/plot_ncut.py
index 941ef014..3160f710 100644
--- a/doc/examples/plot_ncut.py
+++ b/doc/examples/plot_ncut.py
@@ -22,7 +22,7 @@ labels1 = segmentation.slic(img, compactness=30, n_segments=400)
 out1 = color.label2rgb(labels1, img, kind='avg')
 
 g = graph.rag_mean_color(img, labels1, mode='similarity')
-labels2 = graph.cut_n(labels1, g)
+labels2 = graph.cut_normalized(labels1, g)
 out2 = color.label2rgb(labels2, img, kind='avg')
 
 plt.figure()
diff --git a/skimage/graph/graph_cut.py b/skimage/graph/graph_cut.py
index a5a54141..d02dde3a 100644
--- a/skimage/graph/graph_cut.py
+++ b/skimage/graph/graph_cut.py
@@ -65,7 +65,7 @@ def cut_threshold(labels, rag, thresh):
     return map_array[labels]
 
 
-def cut_n(labels, rag, thresh=0.001, num_cuts=10):
+def cut_normalized(labels, rag, thresh=0.001, num_cuts=10):
     """Perform Normalized Graph cut on the Region Adjacency Graph.
 
     Given an image's labels and its similarity RAG, recursively perform
@@ -96,7 +96,7 @@ def cut_n(labels, rag, thresh=0.001, num_cuts=10):
     >>> img = data.lena()
     >>> labels = segmentation.slic(img, compactness=30, n_segments=400)
     >>> rag = graph.rag_mean_color(img, labels, mode='similarity')
-    >>> new_labels = graph.cut_n(labels, rag)
+    >>> new_labels = graph.cut_normalized(labels, rag)
 
     References
     ----------