Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques are commonly used for this purpose. Unfortunately, these methods provide minimal information about the model structure required to accurately represent process characteristics. In this contribution, we propose the use of Genetic Programming (GP) as a method for developing input-output process models from experimental data. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. This has the advantage that no a priori modelling assumptions have to be made. Moreover, the technique can discriminate between relevant and irrelevant process inputs, yielding parsimonious model structures that accurately represent process characteristics. Following a tutorial example, the usefulness of the technique is demonstrated by the development of steady-state models for two typical processes, a vacuum distillation column and a chemical reactor system. A statistical analysis procedure is used to aid in the assessment of GP algorithm settings and to guide in the selection of the find model structure.