The main purpose of controlling the distillation process is to maintain the composition of the products in an intended specification in order to optimize operating costs and meet market requirements. Line analyzers and laboratory analysis are generally used for composition information. However, such alternatives are expensive, do not provide information in real time and are not always feasible or available. In this context, soft sensors emerge as a cheaper alternative. The aim of this work is to develop a soft sensor to infer online the ethanol content in the products of a batch distillation process of watermelon wine, based on temperature measurements. The developed model consists in a multilayer perceptron artificial neural network with one hidden layer. The Levenberg-Marquardt algorithm was used to optimize the weights. In order to train and validating the ANN, real distillation data of water-alcohol binary mixtures with initial compositions similar to the watermelon wine used. The simulation was also validated with real data of wine distillations. The RMSE was applied in the performance evaluation of the model and its result was 0.029 in mass fraction, showing the feasibility of its use as a soft sensor.