The use of hypothesis verification is recurrent in the model based recognition literature. Small sets of features forming salient groups are paired with model features. Pose can be hypothesised from this small set of correspondences. Verification of the pose consists in measuring how much model features transformed by the computed pose coincide with image features. When data involved in the initial pairing are noisy the pose is inaccurate and verification is a difficult problem. In this paper we propose to use a robust hypothesis verification algorithm to perform object recognition. We explain how to integrate it in two different recognition schemes (2D and 3D recognition). After describing these applications we present numerous experimental results proving the robustness and the efficiency of these algorithms.