In this paper we first propose to use a radial basis function (RBF) network to increase the separation performance of blind signal separation (BSS). The independent component analysis (ICA) is often used for the BSS problem, but in general, the ICA employs the sigmoid function to describe the probability distribution of signal, more precisely the derivative of the logarithmic probability density function (PDF) of signal. In order to enhance the signal separation performance of BSS, we try to describe this nonlinear derivative function as accurately as possible by using RBF network. We further propose a hybrid ICA to make the most of the both advantages of the conventional ICA and the RBF based ICA. The proposed method is applied to several signal separation problems. The effectiveness of the proposed method has been confirmed by simulation experiments.