The most popular neural network models for use in speech recognition experiments are employ model neurons which apply a nonlinear function to a weighted sum of their inputs. These networks are trained by adjusting the weights in the weighted sums. There is another class of models called Boolean networks, in which the model neurons output logical functions of their inputs. The training process adjusts the truth-tables which specify the logical functions. Although less well-known than conventional models, Boolean networks have been studied since 1960's. They have been sufficiently successful to form the basis of a commercial product for classification of images. This is a report on the application of a Boolean network to phoneme classification.