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An object recognition system has been developed which incorporates topological as well as geometric information to match viewpoint dependent object descriptors. Theorem proving techniques are used to produce symbolic pattern matches. The recognition process uses a three phase approach. First, hypotheses are generated which correspond to model descriptors that are likely to match the data. Evidence is applied to viable hypotheses to produce a partial match. The partial match is then used to constrain the full recognition process which leads to object identification. This strategy has been found to strongly constrain the search space of possible matches and leads to large reductions in recognition times. The major contributions of the system are the representation scheme and the use of theorem proving techniques to verify object identities. This approach permits describing objects at a variety of levels and facilitates recognition despite missing information or the inclusion of artifactual data. Results of the recognition process on synthetic and actual laser range data are presented for curved and planar objects. The system is shown to operate with robustness and alacrity.