This paper determines, using artificial neural-networks (ANNs), the performance of and exhaust emissions from a diesel engine with respect to injection pressure, engine speed and throttle position. The design injection-pressure of the diesel engine, for the turbocharger and pre-combustion chamber used, is 150 bar. Experiments have been performed for four pressures, namely 100, 150, 200 and 250 bar with throttle positions of 50, 75 and 100%. Engine torque, power, brake mean effective pressure, specific fuel consumption, fuel flow, and exhaust emissions such as SO2, CO2, NOx, and smoke level (%N) have been investigated. The back-propagation learning algorithm with three different variants, single and two hidden layers, and a logistic sigmoid transfer-function have been used in the network. In order to train the network, the results of these measurements have been used. Injection pressure, engine speed, and throttle position have been used as the input layer; performance values and exhaust emissions characteristics have also been used as the output layer. It is shown that the R-2 values are about 0.9999 for the training data, and 0.999 for the test data; RMS values are smaller than 0.01; and mean % errors are smaller than 8.5 for the test data. (C) 2004 Elsevier Ltd. All rights reserved.