In this paper, we report on recent extensions to a surface matching algorithm based on local 3-D signatures. This algorithm was previously shown to be effective in view registration of general surfaces and in object recognition from 3-D model data bases. We describe extensions to the basic matching algorithm which will enable it to address several challenging, and often overlooked, problems encountered with real data.First, we describe extensions that allow us to deal with data sets with large variations in resolution and with large data sets for which computational efficiency is a major issue. The applicability of the enhanced matching algorithm is illustrated by an example application: the construction of large terrain maps and the construction of accurate 3-D models from unregistered views.Second, we describe extensions that facilitate the use of 3-D object recognition in cases in which the scene contains a large amount of clutter (e.g., the object occupies 1% of the scene) and in which the scene presents a high degree of confusion (e.g., the model shape is close to other shapes in the scene.) Those last two extensions involve learning recognition strategies from the description of the model and from the performance of the recognition algorithm using Bayesian and memory-based learning techniques, respectively.