The paper discusses a class of nonlinear discrete sliding-mode control. The control system is designed on the basis of a discrete Lyapunov function. Part of the equivalent control is estimated by an on-line estimator, which is realised by a recurrent neural network (RNN) because of its outstanding ability for modelling a dynamical process. A real-time iterative learning algorithm is developed and used to train the RNN. Unlike the conventional learning algorithms for RNNs, the proposed algorithm ensures that the learning error converges to zero. As a result, the stability of the control system is always assured. In addition, this learning algorithm can be applied for on-line estimation. The proposed controller eliminates chattering and provides sliding-mode motion on the selected manifolds in the state space. Numerical examples are given and simulation results strongly demonstrate that the control scheme is very effective.