In this paper, the modelling of deep-drawing processing using neural networks is established. The relationships between process parameter (material thickness, punch diameter, die-cavity diameter and materials-clearance ratio) and deep-drawing performance (the dimensional error of diameter and cylinder) are created, based on a neural network. A simulated annealing (SA) optimisation algorithm with a performance index is then applied to the neural network to search for the optimal design parameters of the drawing-die. Experimental results have shown that deep-drawing performance can be enhanced by using this approach.