This paper presents a new criterion for viewpoint selection in the context of active Bayesian object recognition and pose estimation. Recognition is performed by probabilistically fusing successive observations with the current belief state of the system. Based on the current belief state, the next viewpoint is chosen to maximize the expected discriminability of the current competing hypotheses. Experiments on a difficult database of aircraft models show that this approach achieves comparable recognition performance to the widely used information theoretic approaches at a much lower computational cost.