diff --git a/doc/user-guide/indexing.rst b/doc/user-guide/indexing.rst
index 492316f898f..90b7cbaf2a9 100644
--- a/doc/user-guide/indexing.rst
+++ b/doc/user-guide/indexing.rst
@@ -352,7 +352,6 @@ dimensions:
     ind_x = xr.DataArray([0, 1], dims=["x"])
     ind_y = xr.DataArray([0, 1], dims=["y"])
     da[ind_x, ind_y]  # orthogonal indexing
-    da[ind_x, ind_x]  # vectorized indexing
 
 Slices or sequences/arrays without named-dimensions are treated as if they have
 the same dimension which is indexed along:
@@ -399,6 +398,12 @@ These methods may also be applied to ``Dataset`` objects
 Vectorized indexing may be used to extract information from the nearest
 grid cells of interest, for example, the nearest climate model grid cells
 to a collection specified weather station latitudes and longitudes.
+To trigger vectorized indexing behavior
+you will need to provide the selection dimensions with a new
+shared output dimension name. In the example below, the selections
+of the closest latitude and longitude are renamed to an output
+dimension named "points":
+
 
 .. ipython:: python