Samples of automotive ethanol, marketed in the northern and eastern regions of the state of Paraná, Brazil, underwent physical and chemical tests. Rates were assessed by Multilayer Perceptron (MLP) neural network for classification. For network training, two hundred epochs, a 0.05 learning rate and a random subdivision of samples in three groups with 70 for training, 15 for test and 15% for validation were employed. Sixty networks were trained from three different initializations. Three networks, one at each start-up, were highlighted and the one with the best performance presented 8 neurons in the hidden layer, with 95 accuracy training, 96 in the test and 96% in validation. The most important variables in classifications, identified by the network, occurred in the following order: alcohol content, density, pH and electrical conductivity. Application of MLP segmented ethanol samples and identified the commercialization regions.