The most important factor in configuring an optimum radial basis function (RBF) network is the appropriate selection of the number of neural units in the hidden layer. This paper proposes a novel algorithm called the scattering-based clustering (SBC) algorithm, in which the frequency sensitive competitive learning (FSCL) algorithm is first applied to let the neural units converge. Scatter matrices of the clustered data are then used to compute the sphericity for each k, where k is the number of clusters. The optimum number of neural units to be used in the hidden layer is then obtained. A comparative study is done between the SBC algorithm and rival penalizes competitive learning (RPCL) algorithm, and the result shows that the SBC algorithm outperforms other algorithms such as CL, FSCL, and RPCL.