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Use of EGR (Exhaust Gas Recirculation) and after-treatment devices allows diesel engines to comply with actual emission regulations. In order to satisfy future emission standards it will be necessary a careful analysis of peculiarities and limits of the current systems for pollution control and of their possible influence on production of other harmful substances. Engine control maps determine optimal EGR considering a trade-off between NOx and smoke emissions. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle number density, which has a great importance for the optimal functioning of after-treatment systems. In this paper a soft computing model that gives real time information on the characteristic of exhaust particles, is proposed. The model, by using a neural network approach, is able to provide information on the effect of EGR on particulate mass concentration and particle size distribution.