Utility of product variants is a nonlinear function of product features. Such a utility function can be represented by a multi-layer perceptron and embedded into the classical logit demand function. However, the utility (which is the output of the multi-layer perceptron to be learned) is not explicitly known. This is why the backpropagation-learning rule has been extended to fit the demand function directly to observed market shares. Forecasts of market shares on the German automobile market with help of perceptron-based and classical logit model are compared. The perceptron-based model leads to a significant improvement of the forecast quality.