diff --git a/doc/examples/plot_adapt_rgb.py b/doc/examples/plot_adapt_rgb.py
new file mode 100644
index 00000000..330e0e2e
--- /dev/null
+++ b/doc/examples/plot_adapt_rgb.py
@@ -0,0 +1,107 @@
+"""
+=========================================
+Adapting gray-scale filters to RGB images
+=========================================
+
+There are many filters that are designed work with gray-scale images but not
+color images. To simplify the process of creating functions that can adapt to
+RGB images, scikit-image provides the ``adapt_rgb`` decorator.
+
+To actually use the ``adapt_rgb`` decorator, you have to decide how you want to
+adapt the RGB image for use with the gray-scale filter. There are two
+pre-defined handlers:
+
+``each_channel``
+    Pass each of the RGB channels to the filter one-by-one, and stitch the
+    results back into an RGB image.
+``hsv_value``
+    Convert the RGB image to HSV and pass the value channel to the filter.
+    The filtered result is inserted back into the HSV image and converted
+    back to RGB.
+
+Below, we demonstrate the use of ``adapt_rgb`` on a couple of gray-scale
+filters:
+"""
+from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
+from skimage import filter
+
+@adapt_rgb(each_channel)
+def sobel_each(image):
+    return filter.sobel(image)
+
+@adapt_rgb(hsv_value)
+def sobel_hsv(image):
+    return filter.sobel(image)
+
+"""
+We can use these functions as we would normally use them, but now they work
+with both gray-scale and color images. Let's plot the results with a color
+image:
+"""
+
+from skimage import data
+import matplotlib.pyplot as plt
+
+image = data.lena()
+
+fig, (ax_each, ax_hsv) = plt.subplots(ncols=2)
+ax_each.imshow(sobel_each(image))
+ax_hsv.imshow(sobel_hsv(image))
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Notice that the result for the value-filtered image preserves the color of the
+original image, but channel filtered image combines in a more surprising way.
+In other common cases, smoothing for example, the channel filtered image will
+produce a better result than the value-filtered image.
+
+You can also create your own handler functions for ``adapt_rgb``. To do so,
+just create a function with the following signature::
+
+    def handler(image_filter, image, *args, **kwargs):
+        # Manipulate RGB image here...
+        image = image_filter(image, *args, **kwargs)
+        # Manipulate filtered image here...
+        return image
+
+Note that ``adapt_rgb`` handlers are written for filters where the image is the
+first argument.
+
+As a very simple example, we can just convert any RGB image to grayscale and
+then return the filtered result:
+
+"""
+from skimage.color import rgb2gray
+
+def as_gray(image_filter, image, *args, **kwargs):
+    gray_image = rgb2gray(image)
+    return image_filter(gray_image, *args, **kwargs)
+
+"""
+It's important to create a signature that uses ``*args`` and ``**kwargs`` to
+pass arguments along to the filter so that the decorated function is allowed to
+have any number of positional and keyword arguments.
+
+Finally, we can use this handler with ``adapt_rgb`` just as before:
+"""
+
+@adapt_rgb(as_gray)
+def sobel_gray(image):
+    return filter.sobel(image)
+
+fig, ax = plt.subplots()
+ax.imshow(sobel_gray(image))
+
+plt.show()
+
+"""
+.. image:: PLOT2RST.current_figure
+
+.. note::
+
+    A very simple check of the array shape is used for detecting RGB images, so
+    ``adapt_rgb`` is not recommended for functions that support 3D volumes or
+    color images in non-RGB spaces.
+
+"""