The use of stochastic formalisms, such as Stochastic Automata Networks (SAN), can be very useful for statistical prediction and behavior analysis. Once well fitted, such formalisms can generate probabilities about a target reality.These probabilities can be seen as a statistical approach of knowledge discovery.However, the building process of models for real world problems is time consuming even for experienced modelers. Furthermore, it is often necessary to be a domain specialist to create a model.This work illustrates a new method to automatically learn simple SAN models directly from a data source.This method is encapsulated in a tool called SAN GEnerator (SANGE). Through examples we show how this new model fitting method is powerful and relatively easy to use; therefore this can grant access to a much broader community to such powerful modeling formalisms.