Abstract This paper addresses attitudes and forms of process modelling in biochemical engineering. Baker's yeast production in a fed-batch fermenter, at laboratory scale, is employed as case-study. Three modelling approaches are described and compared, viz. — the conventional mechanistic approach, formulations based on different artificial neural network (ANN) topologies and a hybrid mechanistic-ANN structure. A standard 2-step procedure of model development, estimation (training) and validation with two independent sets of experiments, has been carried out. The mechanistic model, using reaction kinetic schemes from the literature, fine tuned by classical non-linear regression, gave smooth predictions for the validation data runs, but showed limited ability in predicting the test data. The ANN were able to describe experiments at the training stage, but failed the validation (i.e. extrapolation) procedure, giving oscillatory predictions of the process state. Additionally, this approach suffers from a strong influence of the net parameters, which must be chosen by trial and error. The hybrid model predictions are good with the training and very satisfactory with the experimental test data. The indication is that the latter is a powerful tool for process modelling in biochemical engineering, particularly when limited theoretical knowledge of the process is available.