Deep drawing is one of the most crucial processes in sheet metal forming. As for the multi-stage deep drawing process, because of many design parameters it comprises, predicting process results is a difficult and time-consuming task. In this study, to predict process results, the deep neural network was proposed. Seven design parameters were set and their range was determined with references to empirical formulas. Then, we determined prediction outputs, comprising maximum effective strain, minimum thickness, and bottom mean thickness. Five-hundred sampling points were determined using latinhypercube sampling method. According to the sampling points, finite element analysis was conducted to achieve process results. From the data rendered by the finite element analysis, the deep neural network was trained. Then, the deep neural network was tested with an additional 80 test samples to evaluate performance, and its performance was compared with radial basis function kernel support vector regression. The results showed that the relative performance of the deep neural network was superior to support vector regression.