Two of the major challenges of the European Union policy are to reduce both greenhouse gas (GHG) emissions and the dependence on fossil-based fuels. Biodiesel, produced through the transesterification of vegetable oils or animal fats, seems to be an excellent support nowadays, new raw materials to produce biodiesel are under study, provided that the production of biodiesel from edible oils is controversial for some social organizations. Olive pomace oil derives from the oil left in the olive fruit pulp (once olive oil has been extracted) and may be an option to considerer since it exhibits high neutral flavor that makes it undesirable for consumption, unless it is treated and further blended with virgin olive oil. Therefore, other uses for this oil could be soap production or its recycling to produce biodiesel. In this paper, noise emissions of a direct injection diesel engine Perkins fueled with olive pomace oil methyl ester (OPME) at several steady-state engine operating conditions were studied. This study proposes the use of different approaches for sound prediction of a diesel engine, based on artificial neural network (such as Evolutionary Product Unit Neural Networks (PUNNs) and Radial Basic Function Neural Networks (RBFNNs)) and response surface models. Their accuracies are compared in terms of Mean Square Error (MSE) and Standard Error of Prediction (SEP). It can be concluded that the use of PUNN improves the accuracy achieving acceptable values for both MSE and SEP by means of a simpler model than the combined PU and RBF NN proposed model. Moreover, it was found that the variable power does not explain the noise value prediction, the noise emitted by the engine is inversely related to the 1/3rd octave band of the frequency value and diesel fuel noise plays the most important role and influence in the PUNN model. Response surface models are rejected, due to their unacceptable accuracy in terms of noise prediction. (C) 2012 Elsevier Ltd. All rights reserved.