Artificial neural networks have been used for the correlation and prediction of vapor-liquid equilibrium data of binary water mixtures found in alcoholic beverage production. The main interest of the study is the accurate modeling of the bubble pressure and concentration of congeners in the vapor phase (substances different from ethanol and water), considered to be an important enological parameter in the alcoholic industry. Nine binary water+congener mixtures were considered for analysis. Vapor-liquid equilibrium data of these systems were taken from the literature (333 data points for training and 111 data points for testing), the artificial neural network results were compared with available literature data, and the accuracy of the modeling is discussed. The study shows that the neural network model is a good alternative method for the estimation of phase equilibrium properties for this type of mixture. Supplemental materials are available for this article. Go to the publisher's online edition of Chemical Engineering Communications to view the free supplemental file. http://www.informaworld.com/smpp/title similar to db=all similar to content=t713454788.