In this paper an artificial neural network (ANN) is used to predict the thickness along a cup wall in hydromechanical deep drawing. This model uses a feed-forward backprorogation neural network. After using the experimental results to train and test the network, the model was applied to new data for the prediction of thickness strains in hydro-mechanical deep drawing. The results are promising. In the present work, we also attempt to perform a finite element simulation of the process for the two dimensional axi-symmetric case using explicit finite element code LS-DYNA 2D. Counter pressure on the blank is applied by specifying the pressure boundary conditions. A comparison was made between simulated, experimental and ANN results of hydro-mechanical deep drawing using low carbon extra deep drawing grade steel sheets of 0.96 mm thickness. It was also found that by hydro-mechanical deep drawing, a higher drawability and a more uniform thickness distribution were obtained when compared to conventional deep drawing.