In Adaptive Control Theory there are different procedures to identify a linear system. The fundamental problem is that in the real world many systems are nonlinear and it is not easy to obtain a mathematical model. In this work, an identification procedure for nonlinear systems is presented using the properties of Artificial Neural Networks and Genetic Algorithms to optimize the architecture of the network. A new technique of Adaptive Control to cancel the nonlinear dynamics of the system is proposed to set the poles of the system in a desire position. The behavior of the algorithm for the linear and nonlinear case is presented with the analysis of the theory and operational importance of these techniques.