In this paper we present a statistical approach based on evolving Fuzzy-rule-based (FRB) classifiers for the development of dialog managers for spoken dialog systems. The dialog managers developed by means of our proposal select the next system action by considering a set of dynamic fuzzy rules that are automatically obtained by means of the application of the FRB classification process. Our approach has the main advantage of taking into account the data supplied by the user throughout the complete dialog history without causing scalability problems, also considering confidence measures provided by the recognition and understanding modules. The use of EFS also allows to process streaming data on-line in real time, thus dynamically evolving the structure and operation of the dialog model based on the interaction of the dialog system with its users. We also describe the application of our proposal to develop a dialog system providing railway information.