This study demonstrates the successful use of back-propagation artificial neural networks (BPANNs) in predicting the shrinkage and warpage of injection-molded thin-wall parts. The effects of structural parameters of a BPANN on the prediction accuracy and the capability of a BPANN in determining the optimal process condition are also discussed. The training and testing data are obtained experimentally based on a Taguchi L-27(3(13)) test schedule. The results show that the trained BPANN can successfully predict the shrinkage and warpage of injection-molded thin-wall parts. Comparing the prediction accuracies of the trained BPANN and C-Mold software, it is noted that the trained BPANN predicts more accurately. In terms of determining the optimal process condition for minimizing the shrinkage and warpage of injected thin-wall parts, the trained BPANN is also shown to give a better optimal process coildition than Taguchi's method. (C) 2004 Society of Plastics Engineers.