In order to achieve successful recognition in practical model-based vision applications, many diverse sources of knowledge need to be exploited. By incorporating such information sources as sensor phenomenology, viewing geometry, model part geometry, scene environment, and external situational constraints, we can better recognize parameterized and obscured objects, resolve conflicting hypotheses, and derive the scene interpretation in an efficient manner. A Bayesian modeling and reasoning approach is well-suited for solving this problem by satisfying these goals: integration of diverse evidence sources in an order-independent manner; efficient manipulation of the system for opportunistic control; rational reasoning behavior in terms of graceful degradation as uncertainty increases; and explanation of results in terms of domain properties. We have developed a general model-based recognition Bayesian modeling and reasoning paradigm and tested it by applying it to two model-based recognition problems: military battlefield scene analysis and vehicle classification. The capabilities of this modeling representation include: hierarchical reasoning (coarse to fine, sub-part to whole) for favorable combinatorics; multiple belief propagation strategies for adapting to complex feature interactions; incorporation of static and dynamic constraints; and probabilistic reasoning over both discrete and continuous variables.