In this paper we compare the performance of local detectors and descriptors in the context of object class recognition. Recently, many detectors / descriptors have been evaluated in the context of matching as well as invariance to viewpoint changes (Mikolajczyk & Schmid, 2004). However, it is unclear if these results can be generalized to categorization problems, which require different properties of features. We evaluate 5 stateof- the-art scale invariant region detectors and 5 descriptors. Local features are computed for 20 object classes and clustered using hierarchical agglomerative clustering. We measure the quality of appearance clusters and location distributions using entropy as well as precision. We also measure how the clusters generalize from training set to novel test data. Our results indicate that extended SIFT descriptors (Mikolajczyk & Schmid, 2005) computed on Hessian-Laplace (Mikolajczyk & Schmid, 2004) regions perform best. Second score is obtained by Salient regions (Kadir & Brady, 2001). The results also show that these two detectors provide complementary features. The new detectors/ descriptors significantly improve the performance of a state-of-the art recognition approach (Leibe et al., 2005) in pedestrian detection task.