Object recognition in range image data is formulated as template set matching. The object model is represented as a set of voxel templates, one for each possible pose. The set of all templates is composed into a binary decision tree. Each leaf node references a small number of templates. Each internal node references a single voxel, and has two branches, T and F. The sub-tree branching from the T branch contains the subset of templates which contain the node voxel.Conversely, the sub-tree branching from F branch contains the subset of templates which do not contain the node voxel. Traversing the tree at any image location executes a point probe strategy. It efficiently determines a good match with the template set by interrogating only those elements which discriminate between the remaining possible interpretations.The method has been implemented for a number of different heuristic tree design and traversal methods. Results are presented of extensive tests for two objects under isolated, cluttered, and occluded scene conditions. It is shown that there exist traversal/design combinations which are both efficient and reliable, and that the method is robust.