Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency is proved from a mild set of assumptions. A number of applications within statistics, decision theory, and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given.