The article describes an approach for the self organizing generation of models of complex and unknown processes by means of genetic programming and its application in a biotechnological fed batch production. The approach described combines novel results of computer science-genetic programming-with well known and proven techniques of control and system theory-block diagrams and Z transformation. The synthesis of these approaches is a powerful tool for data driven modelling that offers a large number of possibilities to integrate existing knowledge e.g. on submodels or expected elements. The models received by the use of this tool provide a transparent insight into the structure of the process and a basis for long term prediction of the process behaviour and therefore for the determination of optimal setpoint profiles. That means that this approach may overcome the specific difficulties that are bound to the use of adaptive or learning-in the sense of neural networks-methods.