Artificial neural network prediction of wear rate and friction coefficient of brake pads developed from palm kernel fibres (PKF) was carried out in this study. Major input parameters including materials formulation, manufacturing conditions, and operating conditions were introduced into the neural model while wear rate and friction coefficient were the outputs. The network architecture of LM12 [4-3]2 2 was selected for predicting the wear rate and coefficient of friction. The predicted wear rate and friction coefficient using ANN models were compared with the measured values using some statistical indicators. Results showed that the ANN predicted accurately the wear rate and friction coefficient of the developed automotive brake pads.
