A new approach is proposed to model nonlinear dynamic systems by combining SOM (self-organizing feature map) with support vector regression (SVR) based on expert system. The whole system has a two-stage neural network architecture. In the first stage SOM is used as a clustering algorithm to partition the whole input space into several disjointed regions. A hierarchical architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVR, also called SVR experts, that best fit each partitioned region by the combination of different kernel function of SVR and promote the configuration and tuning of SVR. Finally, to apply this new approach to time-series prediction problems based on the Mackey-Glass differential equation and Santa Fe data, the results show that SVR experts has effective improvement in the generalist performance in comparison with the single SVR model.