diff --git a/doc/examples/plot_watershed.py b/doc/examples/plot_watershed.py
new file mode 100644
index 00000000..0fea79f2
--- /dev/null
+++ b/doc/examples/plot_watershed.py
@@ -0,0 +1,59 @@
+"""
+======================
+Watershed segmentation
+======================
+
+The watershed is a classical algorithm used for **segmentation**, that is, for 
+separating different objects in an image.
+
+Starting from user-defined markers, the watershed algorithm treats pixels
+values as a local topography (elevation). The algorithm floods basins from the
+markers, until basins attributed to different markers meet on watershed lines.
+In many cases, markers are chosen as local minima of the image, from which
+basins are flooded.
+
+In the example below, two overlapping circles are to be separated. To do so,
+one computes an image that is the distance to the background. The maxima of
+this distance (i.e., the minima of the opposite of the distance) are chosen as
+markers, and the flooding of basins from such markers separates the two circles
+along a watershed line.
+
+See http://en.wikipedia.org/wiki/Watershed_(image_processing) for more details
+on the algorithm.
+"""
+
+import numpy as np
+from scipy import ndimage
+import matplotlib.pyplot as plt
+from scikits.image.morphology import watershed, is_local_maximum
+
+# Generate an initial image with two overlapping circles
+x, y = np.indices((80, 80))
+x1, y1, x2, y2 = 28, 28, 44, 52
+r1, r2 = 16, 20
+mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
+mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
+image = np.logical_or(mask_circle1, mask_circle2)
+# Now we want to separate the two objects in image
+# Generate the markers as local maxima of the distance
+# to the background
+from scipy import ndimage
+distance = ndimage.distance_transform_edt(image)
+local_maxi = is_local_maximum(distance, image, np.ones((3, 3)))
+markers = ndimage.label(local_maxi)[0]
+labels = watershed(-distance, markers, mask=image)
+
+plt.figure(figsize=(9, 3))
+plt.subplot(131)
+plt.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
+plt.axis('off')
+plt.subplot(132)
+plt.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
+plt.axis('off')
+plt.subplot(133)
+plt.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest')
+plt.axis('off')
+
+plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
+                    right=1)
+plt.show()
diff --git a/scikits/image/morphology/watershed.py b/scikits/image/morphology/watershed.py
index a159a6ab..3b72d834 100644
--- a/scikits/image/morphology/watershed.py
+++ b/scikits/image/morphology/watershed.py
@@ -91,10 +91,6 @@ def fast_watershed(image, markers, connectivity=None, offset=None, mask=None):
 
     .. [2] http://cmm.ensmp.fr/~beucher/wtshed.html
 
-    See also
-    --------
-    `is_local_maximum`, `scipy.ndimage.label`
-
     Examples
     --------
     The watershed algorithm is very useful to separate overlapping objects