Multilayered neural networks are used to construct nonlinear learning control systems for a class of unknown nonlinear systems in a canonical form. An adaptive output tracking architecture is proposed using the three-layered neural networks which are trained by back-propagation algorithm. The closed-loop system is proved to be stable

with the output tracking error converging to the δ-neighbourhood of the origin.