To be practical, recognition systems must deal with uncertainty. Positions of image features in scenes vary. Features sometimes fail to appear because of unfavorable illumination. In this work, methods of statistical inference are combined with empirical models of uncertainty in order to evaluate and refine hypotheses about the occurrence of a known object in a scene. Probabilistic models are used to characterize image features and their correspondences. A statistical approach is taken for the acquisition of object models from observations in images: Mean Edge Images are used to capture object features that are reasonably stable with respect to variations in illumination.