This study details the artificial neural network modelling of a diesel engine to predict the torque, power, brake-specific fuel consumption, and pollutant emissions, including carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbons, and filter smoke number. To collect data for training and testing the neural network, experiments were performed on a four cylinder, four stroke compression ignition engine. A total of 108 test points were run on a dynamometer. For the first part of this work, a parameter packet was used as the inputs for the neural network, and satisfactory regression was found with the outputs (over similar to 95%), excluding total hydrocarbons. The second stage of this work addressed developing new networks with additional inputs for predicting the total hydrocarbons, and the regression was raised from 75% to 90%. This study shows that the artificial neural network approach can be used for accurately predicting characteristic values of an internal combustion engine and that the neural network performance can be increased using additional related input data.