The adoption of numerical models, such as Artificial Neural Networks, to reproduce the behavior of a physical system basing only on data-driven approach, offers a powerful technique in simulation environment. This master thesis work exploits this approach to develop Artificial Neural Network models aimed at simulating the functioning of a Diesel Oxidation Catalyst, one of the first after-treatment system's component present into exhaust line of diesel engine applications. The study begins with the generation of several Design of Experiments to simulate most common operating conditions by means of pre-existing physics-based 1D model to collect data, which serve as the foundation for the construction of several single-output models.