Modern automotive petrol engine performance is significantly affected by effective tune-up. The practice of engine tune-up relies on the experience of the automotive engineer, and tune-up is done by trial-and-error method and then the vehicle engine is run on the dynamometer to show the actual engine performance. And the practice involves a large amount of time and money, and fail to tune up the engine optimally because a formal performance model of the engine was not determined yet. With an emerging technique, support vector machines (SVM), the approximate performance model of a petrol vehicle engine can be determined by training the sample engine performance data acquired from the dynamometer. The number of dynamometer tests for an engine tune-up can be reduced because the estimated engine performance model can replace the dynamometer tests to a certain extent. The construction, validation and accuracy of the model are discussed. The study shows that the predicted results agree well with the actual test results. To illustrate the significance of the SVM methodology, the results were also compared with that regressed using multiplayer feedforward neural networks.