The present paper shows that a wide class of complex transfer functions encountered in geophysics can be efficiently modeled using neutral networks. Neural networks can approximate numerical and nonnumerical transfer functions. They provide an optimum basis of nonlinear functions allowing a uniform approximation of any continuous function. Neural networks can also realize classification tasks. It is shown that the classifier mode is related to Bayes discriminant functions, which give the minimum error risk classification. This mode is useful for extracting information from an unknown process. These properties are applied to the ERS1 simulated scatterometer data. Compared to other methods, neural network solutions are the most skillful. ¿ American Geophysical Union 1993