From the dual structural radial basis function network (DSRBF) (Cheung and Xu 2001), this paper presents a new divide-and-conquer learning approach to radial basis function networks (DCRBF). The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which takes a sub-input space as its input. Since this system divides a high-dimensional modeling problem into serveral low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning is much faster. We have experimentally shown its outstanding learning performance on forecasting two real time series as well as synthetic data in comparison with a conventional RBF one.