This paper argues that if phonological and phonetic phenomena found in language data and in experimental data all have to be accounted for within a single framework, then that framework will have to be based on neural networks. We introduce an artificial neural network model that can handle stochastic processing in production and comprehension. With the “inoutstar” learning algorithm, the model is able to handle two seemingly disparate phenomena at the same time: gradual category creation and auditory dispersion. As a result, two aspects of the transmission of language from one generation to the next are integrated in a single model. The model therefore addresses the hitherto unsolved problem of how symbolic-looking discrete language behaviour can emerge in the child from gradient input data from her language environment. We conclude that neural network models, besides being more biologically plausible than other frameworks, hold a promise for fruitful theorizing in an area of linguistics that traditionally assumes both continuous and discrete levels of representation.