Electronic engine controls based on real time diagnosis of combustion process can significantly help incompling with the stricter and stricter regulations on pollutants emissions and fuel consumption.Themost important parameter for hee valuation of combustion quality in internal combustion engine sisthein-cylinder pressure, butits direct measurement is very expensive and involves an intrusive approach to the cylinder.Previous researches demonstrated the direct relationship existing between in-cylinder pressure and engine crankshaft speed and several authors triedtore construct the pressure cycle on the basis of the engine speed signal. In this paper we propose the use of a Multi-Layer Percept ronneural network to model the relationship between the engine crank shaft speed and some parameters derived from the in-cylinder pressure cycle.This allows to have an on-intrusive estimation of cylinder pressure and are altime evaluation of combustion quality. Thestructureofthemodel and thetrainingprocedureisoutlinedinthepaper.Apossiblecombustioncontroller usingtheinformationextractedfromthecrankshaftspeedinformationisalsoproposed. The application of the neural network model is demonstrated on a single-cylinder spark ignition engine tested in a wider ange of speeds and loads.Results confirm tha ta good estimation of some combustion pressure parameters can be obtained by means of a suitable processing of crank shaft speed signal.