An improved parallel recurrent neural network with canonical architecture, named Recurrent Trainable Neural Network (RTNN) and an error based backpropagation through time learning algorithm, are applied to a D.C. motor drive identification and control. The unknown nonlinear dynamics of the motor together with the load are identified by the RTNN. The trained RTNN identifier is combined with a desired reference model and a RTNN controller in a direct adaptive control scheme, so in order to achieve a desired trajectory tracking of the motor speed and position. Finally, the applicability of the RTNN topology and learning is illustrated by experimental results.