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In this article, we present an approach for the construction of a stochastic dialog manager, in which the system answer is selected by means of a classification procedure. In particular, we use neural networks for the implementation of this classification process, which takes into account the data supplied by the user and the last system turn. The stochastic model is automatically learnt from training data which are labeled in terms of dialog acts. An important characteristic of this approach is the introduction of a partition in the space of sequences of dialog acts in order to deal with the scarcity of available training data. This system has been developed in the DIHANA project, whose goal is the design and development of a dialog system to access a railway information system using spontaneous speech in Spanish. An evaluation of this approach is also presented.