diff --git a/changelog/128.doc.rst b/changelog/128.doc.rst
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+Added a tutorial (:ref:`sphx_glr_generated_gallery_rgb_composite.py`) demonstrating how to create an RGB image with three different maps.
diff --git a/examples/rgb_composite.py b/examples/rgb_composite.py
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+"""
+=============================
+Making an RGB composite image
+=============================
+
+This example shows the process required to create an RGB composite image
+of three AIA images at different wavelengths. To read more about the
+algorithm used in this example, see this
+`Astropy tutorial <https://docs.astropy.org/en/stable/visualization/rgb.html>`__.
+"""
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+import sunpy.data.sample
+from astropy.visualization import make_lupton_rgb
+from sunpy.map import Map
+
+from sunkit_image.enhance import mgn
+
+###############################################################################
+# We will use three AIA images from the sample data at the following
+# wavelengths: 171, 193, and 211 Angstroms. The 171 image shows the quiet
+# solar corona, 193 shows a hotter region of the corona, and 211 shows
+# active magnetic regions in the corona.
+
+maps = Map(sunpy.data.sample.AIA_171_IMAGE, sunpy.data.sample.AIA_193_IMAGE, sunpy.data.sample.AIA_211_IMAGE)
+
+###############################################################################
+# Before the images are assigned colors and combined, they need to be
+# normalized so that features in each wavelength are visible in the combined
+# image. We will apply multi-scale Gaussian normalization using
+# `sunkit_image.enhance.mgn` to each map and then create the rgb composite.
+# The ``k`` parameter is a scaling factor applied to the normalized image. A
+# value of 5 produces sharper details in the transformed image. In the
+# `~astropy.visualization.make_lupton_rgb` function, ``Q`` is a softening
+# parameter which we set to 0 and ``stretch`` controls the linear stretch
+# applied to the combined image.
+
+maps_mgn = [Map(mgn(m.data, k=5), m.meta) for m in maps]
+im_rgb = make_lupton_rgb(maps_mgn[0].data, maps_mgn[1].data, maps_mgn[2].data, Q=0, stretch=1)
+
+###############################################################################
+# The output of the `astropy.visualization.make_lupton_rgb` algorithm is not
+# a Map, but instead an image. So, we need to create a WCS Axes using one of
+# original maps and manually set the label. In the first step below, we grab
+# the Set1 qualitative colormap to apply to the custom legend lines.
+
+cmap = plt.cm.Set1
+custom_lines = [
+    Line2D([0], [0], color=cmap(0), lw=4),
+    Line2D([0], [0], color=cmap(2), lw=4),
+    Line2D([0], [0], color=cmap(1), lw=4),
+]
+fig = plt.figure()
+ax = fig.add_subplot(111, projection=maps[0].wcs)
+im = ax.imshow(im_rgb)
+lon, lat = ax.coords
+lon.set_axislabel("Helioprojective Longitude")
+lat.set_axislabel("Helioprojective Latitude")
+ax.legend(custom_lines, ["AIA 171", "AIA 193", "AIA 211"])
+ax.set_title("AIA RGB Composite")
+
+plt.show()