Fermentations involving competition between two or more kinds of cells under nonideal conditions show complex profiles that are sensitive to the extra-cellular environment. These fermentations therefore require accurate and rapid on-line data acquisition and control. However, :both on-line measurements and modelling are difficult and expensive for large bioreactors, thus limiting the usefulness of model-based control. While neural networks offer an alternative, they require extensive training and can be-difficult to optimize for large arrays. Hybrid networks combining a few neural networks with, some mathematical equations-offer a good compromise. The possibility of using a hybrid model for simulation-cum-control has been examined here for the fed-batch production of streptokinase. Under noideal conditions, hybrid neural models outperformed both mathematical models and arrays of neural networks, thus suggesting their viability for large-scale fermentation monitoring. and control.