A neural-network based model of the kinetics in a fermentation process is presented. In the development and application of the model, the state of the process was estimated by an independent model based on elemental balances and an electroneutrality condition. The aim of the work was a model that describes the overall kinetic relations well enough to be used for on-line simulation and prediction. Both feed-forward and partially recurrent networks were investigated. The networks were trained off-line, but in the on-line application they are adapted to the process conditions by adjusting a set of correction factors. The approach is illustrated on a number of batch runs of Saccharomyces cerevisiae, where the networks are demonstrated to be able to accurately describe the biphasic growth.