In this paper, we propose a real-time distributed framework for composite event recognition in a calibrated pan-tilt camera network. A composite event comprises of events that occur simultaneously or sequentially at different locations across time. Distributed composite event recognition requires distributed multi-camera multi-object tracking and distributed multi-camera event recognition. We apply belief propagation to reach a consensus on the global identities of the objects in the pan-tilt camera network and to arrive at a consensus on the event recognized by multiple cameras simultaneously observing it. We propose a hidden Markov model based approach for composite event recognition. We also propose a novel probabilistic Latent Semantic Analysis based algorithm for pair-wise interaction recognition and present an application of our distributed composite event recognition framework, where the events are interactions between pairs of objects. © 2010 ACM.