This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality. The accuracy of the predicted pressure signals is analyzed in terms of mean square error and of a number of parameters, such as maximum pressure, peak location, and mass burned fraction (MBF). Robustness is sought with respect to changes in the engine parameters as well as with respect to changes in the nature of the fuel. The encouraging results indicate that the prediction model based on RBF neural network can be incorporated in the design of fuel-independent real-time control of diesel engines.