This paper focuses on using radial basis function (RBF) network classifiers to solve the large-scale learning problems. Above all, a large-scale dataset is divided into multiple limited-scale subsets, and each subset only includes a small part of samples from the original dataset. Naturally, modular single-layer RBF classifiers come into being, in which each module is made up of multiple RBF kernels. The number, locations, widths of kernels may adoptively be determined, and the module with the max output gives the class label of a certain sample. This paper clarifies that a nonlinearly separable problem may still keep so in the kernel space. Two-spirals and letter recognition results show that the proposed method is quite effective.