The problem of estimating and tracking the pose of a 3-D object is a well-established problem in machine vision with important applications in terrestrial and space robotics. This paper describes how 3-D range data, available from a new generation of real-time laser range-finding systems, can be used to solve the pose determination problem. The approach is based on analysis of the local geometric structure encoded in the range data to extract landmarks. Local configurations of these landmarks provide estimates of identity and pose through matching against a nominal models using a Bayesian optimization technique. Aggregates of local estimates are used to provide a robust estimate of global pose. The technique is well-suited to space tracking applications for which examples are provided.