We present a probabilistic, salience-based mechanism for the interpretation of pointing gestures together with spoken utterances. Our formulation models dependencies between spatial and temporal aspects of gestures and features of objects. The results from our corpus-based evaluation show that the incorporation of pointing information improves interpretation accuracy.