The use of a self-organising neural network as a feedforward compensator for robot tracking control applications is proposed. The topology of the input space is adaptively mapped onto a set of neurons where each neuron represents a discrete cell in the input domain. Within each cell, a linear mapping is established between the input and output space. The training of such a network involves training of a weight vector that represents the topology of the input space and weight vectors (action space weights) that linearly code an input pattern to action space. In the first phase of network training, a ＇neural-gas＇ algorithm is employed for learning the topology of the input space while weight vectors representing control action space is learned by backpropagating feedback control action. During this phase of learning, the weights associated with neurons in the neighbourhood of winning neurons are also updated. In the second stage, a recursive least squares based estimation scheme is applied to fine tune the action space weights associated with winning neurons only, without disturbing the input topology map learned in the first phase. The proposed scheme has been compared with multilayered network (MLN) and radial basis function network (RBFN) based inverse dynamics learning schemes. Simulation results show that the proposed scheme has better generalisation capability than both MLN and RBFN.