In the analysis of several scientific and engineering problems, nonlinear dynamic systems are modeled as systems composed by the series connection of a linear dynamic subsystem with a nonlinear memoryless unit. If the non-linearity follows the linear subsystem, the system is called of Wiener type; otherwise, the system is called of Hammerstein type. In this work, a nonparametric approach to the approximation of Wiener/Hammerstein models is proposed. The approach is based on the use of Laguerre filer banks to approximate the linear subsystem, and of an artificial neural network to approximate the memoryless non-linearity. Building on existing results of approximating properties of Laguerre filters and neural networks, theoretical convergence results (as a function of the number of Laguerre filters and neural units) of the approximating scheme to the underlining Hammerstein/Wiener model are reported. It is emphasized that the suggested approach requires much milder assumptions than those needed by other procedures previously proposed in the literature. In particular, no knowledge of the linear system order and time delay is needed, and the non-linearity need not to be invertible and/or of polynomial type.