A multimodal person recognition architecture has been developed for the purpose of improving overall recognition performance and for addressing channel-specific performance shortfalls. This multimodal architecture includes the fusion of a face recognition system with the MIT/LLGMM/UBM speaker recognition architecture. This architecture exploits the complementary and redundant nature of the face and speech modalities. The resulting multimodal architecture has been evaluated on theXM2VTS corpus using the Lausanne open set verification protocols, and demonstrates excellent recognition performance. The multimodal architecture also exhibits strong recognition performance gains over the performance of the individual modalities.