Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, methods to train a WN, initialization algorithms, variable significance and variable selection algorithms, a model selection method and finally methods to construct confidence and prediction intervals. Our proposed framework was tested in two simulated cases and in one real dataset consisting of daily temperatures in Berlin. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.