In this paper we show that ef?cient object recognition can be obtained by combining informative features with linear classi?cation. The results demonstrate the superiority of informative class-speci?c features, as compared with generic type features such as wavelets, for the task of object recognition. We show that information rich features can reach optimal performance with simple linear separation rules, while generic feature based classi?ers require more complex classi?cation schemes. This is signi?cant because ef?cient and optimal methods have been developed for spaces that allow linear separation. To compare different strategies for feature extraction, we trained and compared classi?ers working in feature spaces of the same low dimensionality, using two feature types (image fragments vs. wavelets) and two classi?cation rules (linear hyperplane and a Bayesian Network). The results show that by maximizing the individual information of the features, it is possible to obtain ef?cient classi?cation by a simple linear separating rule, as well as more ef?cient learning.