A set of mild steel thin plate specimens used for automotive industry are used as laboratory samples. Different types of distortions are analyzed. Radial basis function neural network (RBFN) models have been developed to predict transverse shrinkage and longitudinal bending distortion of welded plates. Response surface method is used to set up the experimental parameters matrix. Pulse frequency, pulse width, focal distance, defocus distance, moving speed of welded plates, shielded gas, workpiece temperature fluctuation and laser power fluctuation are used as input variables of these models to increase the prediction accuracy. Six different types of RBFN models have been developed to predict the distortion of welded plates. The best one is selected from them and resulted in better output prediction.