This paper describes the use of recurrent neural networks in the control of a simulated planar two-jointed robot arm. Recurrent networks have feedback connections and thus an inherent memory for dynamics which makes them suitable for dynamic system modelling. A feature of the networks adopted is their hybrid hidden layer which includes both linear and non-linear neurons. This facilitates learning of the inverse dynamics model of the robot which can be thought of as comprising a linear and a non-linear part. Following a brief description of the control problem and alternative PID and computed-torque control schemes, the proposed neural network and neural controller will be detailed. The results presented show the superior ability of the proposed neural control scheme at adapting to changes in the dynamics parameters of the robot. (C) 1998 Elsevier Science Ltd. All rights reserved.