This paper investigates data based modelling of complex nonlinear processes, for which a first principle model useful for process monitoring and control is not available. These empirical models may be used as soft sensors in order to monitor a reaction’s progress, so reducing expensive offline sampling and analysis. Three different data modelling techniques are used, namely Ordinary Kriging, Artificial Neural Networks and Support Vector Regression. A simple case is first used to illustrate the problem, assess and validate the modelling approach, and compare the modelling techniques. Next, the methodology is applied to a photo–Fenton pilot plant to model and predict the reaction progress. The results show promising accuracy even when few training points are available, which results in huge savings of time and cost of the experimental work.