An individual’s voice is hardly ever heard in complete isolation. More commonly, it occurs simultaneously along with other interfering sounds, including those of other overlapping voices. Though there has been a great deal of progress in automatic speaker identification, the majority of past work has focused on the case of non-overlapping speakers. Many of these systems are easily confounded by more realistic scenarios where multiple talkers may be overlapping or speaking simultaneously. Furthermore, the variations due to different acoustic environments in real-world settings are detrimental to well-known systems that aim to separate the features or the acoustic signal of a mixture of talkers. We propose a system that, given multiple acoustic observations, attempts to jointly identify and separate the acoustic features of multiple simultaneous talkers that fall within a library of known individuals. This system uses the probabilistic framework of expectation propagation (EP) to iteratively determine model-based statistics of both individual acoustic features and speaker identity. In our initial study, we demonstrate that this framework exhibits performance that in the upper-bound significantly exceeds that of a sequential method employing blind source separation followed by speaker identification on the estimated source signals.