A neural network controller which is used for controlling unknown discrete-time DARMA systems is described. A two-layered neural network is used to estimate the unknown plant dynamics. The well known Widrow-Hoff delta rule is used as the learning algorithm for this network, to minimise the difference between the plant actual response and that predicted by the neural network. The control law is generated online using a second two-layered neural network, so that the plant output is brought to a desired reference signal. It is proved that the control objective is achieved by the closed-loop system and that the system remains closed-loop stable. Some simulation examples are also presented to evaluate the design.