In this paper, we present a statistical approach for the development of a dialog manager and for learning optimal dialog strategies. This methodology is based on a classification procedure that considers all of the previous history of the dialog to select the next system answer. To evaluate the performance of the dialog system, the statistical approach for dialog management has been extended to model the user behavior. The statistical user simulator has been used for the evaluation and improvement of the dialog strategy. Both the user model and the system model are automatically learned from a training corpus that is labeled in terms of dialog acts. New measures have been defined to evaluate the performance of the dialog system. Using these measures, we evaluate both the quality of the simulated dialogs and the improvement of the new dialog strategy that is obtained with the interaction of the two modules. This methodology has been applied to develop a dialog manager within the framework of 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. We propose the use of corpus-based methodologies to develop the main modules in the dialog system.