A model predictive control strategy based on neural network is presented for a continuous stirred tank reactor(CSTR).A segmentation method was adopted to identify Hammerstein-Wiener model coefficient by least squares support vector machines and then to construct a nonlinear predictive controller which was by a linear optimal component and radial basis function neural networks in series.A nonlinear predictive control algorithm based on least support vector machines Hammerstein-Wiener model was realized by using BP neural network to train predictive input sequences and to solve nonlinear predictive control rules by Quasi-Newton method.The simulation results of CSTR illustrate that this approach is effective tracking and controlling product concentration.