In the present paper, Wavelet Networks, are proven to be, as well as many other neural paradigms, a speci c case of the generic paradigm named Weighted Radial Basis Functions Networks. Moreover, a fair comparison between Wavelet and more traditional WRBF networks for function approximation is attempted, in order to demonstrate that the performance depends only on how good the chosen mother activation function ts" the function itself.