Application of neural networks to model the conversion rates of a heterogeneous oxidation reaction has been investigated oxidation of 2-octanol with nitric acid has been considered as a case study. Due to a more complex and unknown kinetics of the investigated reaction the proposed approach based on application of neural networks is an efficient and accurate tool to solve modelling problems. The elaborated hybrid model as well as the modelling procedure have been described in detail. Learning data used to train the networks have been extracted from the experimental results obtained in an extensive investigation programme performed with a RCl Mettler-Toledo reaction calorimeter. The influence of different operating conditions on the accuracy and flexibility of the obtained results has been investigated and discussed. It has been found that with the proposed approach the behaviour of a semi-batch reactor, i.e. its concentration and heat flow time profiles, can be predicted successfully within a singular series of experiments; however, the generalisation of the neural network approach to all series of experiments was impossible.