A new method for data-based fuzzy system modeling is presented. The approach uses Takagi-Sugeno models and Adaptive Simulated Annealing (ASA) to achieve its goal . The problem to solve is well defined - given a training set containing a finite number of input-output pairs, construct a fuzzy system that approximates the behavior of the real system that originated that set , within a pre-established precision .