This work primarily aims to develop an expert system based on the artificial neural network (ANN) to predict the tensile behaviour of tailor welded blanks (TWBs) made of dual-phase (DP) 590 steel. The work also aims to compare the predictions by ANN models with empirical models and the size of the training data set of the prediction accuracy of these models. The strain hardening exponent n and strength coefficient K are predicted. The results obtained from expert system and empirical models are validated by comparing them with the results obtained from finite element simulations and experiments. It is observed that expert system/ANN predictions based on the full factorial design of experiments (DOE) is better than the ANN predictions based on the orthogonal array and predictions based on the empirical models. With the reduced orthogonal training data, ANN model-based predictions are more accurate than the empirical models in most of the test cases taken outside the training range. Inverse models for predicting the TWB parameter combination for good tensile characteristics are also developed and show promising results. The ANN/expert system developed through full factorial DOE is comparable with the experimental results. The ANN/expert system developed through orthogonal DOE is not comparable with the experimental results. © 2012 Taylor and Francis Group, LLC.