Many problems in computer vision require the knowledge of potential point correspondences between two images. The usual approach for automatically determining correspondences begins by comparing small neighborhoods of high saliency in both images. Since speed is of the essence, most current approaches for local region matching involve the computation of a feature vector that is invariant to various geometric and photometric transformations, followed by fast distance computations using standard vector norms. These algorithms include many parameters, and choosing an algorithm and setting its parameters for a given problem is more an art than a science. Furthermore, although invariance of the resulting feature space is in general desirable, there is necessarily a tradeoff between invariance and descriptiveness for any given task. In this paper we pose local region matching as a classification problem, and use powerful machine learning techniques to train a classifier that selects features from a much larger pool. Our algorithm can be trained on specific domains or tasks, and performs better than the state of the art in such cases. Since our method is an application of boosting, we refer to it as Boosted Region Matching (BOOM).