Hydromechanical deep drawing process is one of the forming processes which is devised for solving the existing problems in conventional processes such as traditional deep drawing process. Hydromechanical deep drawing process has many applications in different industries such as aerospace, military and automobile industries. Higher economic efficiency and flexibility can be achieved by using hydromechanical deep drawing process. In comparasion with covnventional deep drawing process, the part quality and thickness distribution are better in hydromechanical deep drawing process. Hydromechanical deep drawing process is an efficient method for producing of complicated parts. Though hydromechanical deep drawing process has some advantages but this method is faced with some difficulties. The finding of appropriate counter pressure and drawing ratio for different materials and process parameters, is one of the problems which this process confronts with them. The try and error method can be used for solving these problems but this method is very expensive and requires too large time. Also, there is some difficulties in hydromechanical deep drawing process analysis such as variable pressure area in punch stroke and geometric complication of part shape. Considering these difficulties, finding an analytical solution for hydromechanical deep drawing process is a complicated task. Therefore, numerical method and simulation can be suitable way for solving this problem, but this method is so expensive too and a separate solving must be executed for each study case. A simple and quick method for solving this problem can be obtained by using an artificial neural network and implementation of a database in neural network. In this thesis simple and fast solving way for hydromechanical deep drawing process has been proposed by implementation of finite element simulation results in an artificial neural network. Process simulation has been done by using ABAQUS software. An innovative method has been used for acting the liquid pressure load proportion to punch stroke. Forming limit diagram damage criteria has been used for prediction of failure in blank. In this thesis, low carbon steel and aluminium materials have been investigated. Overally, the finite element method is a good way for simulation of hydromechanical deep drawing process, also the artificial neural networks is a fast and simple way for predicting of forming limit in hydromechanical deep drawing process. A good agreement between finite element analysis and artificial neural network results were found. By using hydromechanical deep drawing process, limiting drawing ratio 2.5 and 3.1 can be achieved for aluminium and low carbon steel respectively. Keywords: Hydromechanical deep drawing, Drawing ratio, Artificial neural network, Finite element method