A hybrid neural model was developed for the alcoholic fermentation by Zymomoans mobilis. This model is composed by the mass-balance equations of the process and neural networks, which describe the kinetic rates. Strategies that combines scarce experimental data with approximate models of the process were used to generate new data for the training of the networks, minimizing the number of experiments required. The proposed hybrid neural methodology uses all the information available about the process to deal with the difficulties in the development of the model.