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In this paper we propose a visual speech recognition network based on support vector machines. Each word of the dictionary is modeled by a set of temporal sequences of visemes. Each viseme is described by a support vector machine, and the temporal character of speech is modeled by integrating the support vector machines as nodes into a Viterbi decoding lattice. Experiments conducted on a small visual speech recognition task using very simple features demonstrate a word recognition rate on the level of the best rates previously reported even without training the state transition probabilities in the Viterbi lattices. This proves the suitability of support vector machines for visual speech recognition.