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Based on the ability of nonlinear mapping and generalization, an artificial neural network model for the prediction of mechanical properties of hydrogenated TC21 titanium alloy was established. The input parameters of the neural network model includes temperature tensile testing temperature and hydrogen content. The outputs of the model are mechanical properties namely ultimate tensile strength and tensile yield strength. The accuracy of ANN model was tested by the test sample. It is found that the predicted results are in good agreement with experimental value because of the characters of good fault-tolerance and strong commonality. The trained model can predict the mechanical properties of hydrogenated TC21 alloy under the condition of different experimental temperatures and contents. With the help of application of neural network technology in the field of material preparation process and design, the efficiency can be improved greatly, and the cycle of the actual experiment will be shortened obviously.