In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine. (C) 2013 Elsevier Ltd. All rights reserved.