Abstract For the control of lactic acid batch fermentations, the prediction of the pH over a long time horizon and the determination of the final fermentation time are very useful information. Lactic acid batch fermentations, with uncontrolled pH, are modelled with a feedforward neural network to generate the model (pH versus time) of a reference fermentation of Streptococcus thermophilus strain on skim milk. In essence, the feedforward neural network is used as a general nonlinear model to store the information of a series of well-behaved fermentations. This neural network stores the information of previous fermentations and defines a reference fermentation. This reference fermentation is used to perform a comparison with the actual fermentation for the on-line prediction of future pH values and of the final fermentation time. This time, which occurs at a predetermined pH, is predicted with an accuracy of less than 20% at the onset of fermentation and with a much better accuracy as the fermentation proceeds. The future pH values are predicted with a mean error of 0.05 pH for a 3 hour prediction horizon. The prediction is obtained with four geometrical methods by sliding the curve of the reference fermentation along the curve of the actual fermentation. The procedure for sliding the reference curve depends on the geometrical method used. The distinct advantage of neural networks over other class of models is the plasticity of its structure which allows to easily capture the shape of the fermentation curve.