Iterative alignment is one method for feature-based matching of an image and a model for the purpose of object recognition. The method alternately hypothesizes feature pairings and estimates a viewpoint transformation from those pairings; at each stage a refined transformation estimate is used to suggest additional pairings. This paper extends iterative alignment in the domain of 2D similarity transformations so that it represents the uncertainty in the position of each model and image feature, and that of the transformation estimate. A model describes probabilistically the significance, position, and intrinsic attributes of each feature, plus topological relations among features. A measure of the match between a model and an image integrates all four of these, and leads to an efficient matching procedure called probabilistic alignment. That procedure supports both recognition and a learning procedure for acquiring models from training images. By explicitly representing uncertainty, one model can satisfactorily describe appearance over a wider range of viewing conditions. Thus, when models represent 2D characteristic views of a 3D object, fewer models are needed. Experiments demonstrating the effectiveness of this approach are reported.