Geometric indexing is an efficient method of recovering match hypotheses in modelbased object recognition. Unlike other methods, which search for viewpointinvariant shape descriptors to use as indices, we use a learning method to model the smooth variation in appearance of local feature sets (LFS). Indexing from LFS effectively deals with the problems of occlusion and missing features. The functions learned are probability distributions describing the possible interpretations of each index value. During recognition, this information can be used to select the least ambiguous features for matching. A verification stage follows so that the final reliability and accuracy of the match is greater than that from indexing alone. This approach has the potential to work with a wide range of image features and model types. A full 3-D recognition system has been implemented, and we present an example to demonstrate how the method works with real, cluttered images.