In the present investigation, an artificial neural network (ANN) model is developed to predict the isothermal hot forging behavior of AlCuMgPb aluminum alloy. The inputs of the ANN are deformation temperature, frictional factor, ram velocity and displacement whereas the forging force, barreling parameter and final shape are considered as the output variable. The developed feed-forward back-propagation ANN model is trained with Leven berg–Marquardt learning algorithm. Since the finite element (FE) simulation of the process is a time-consuming procedure, the ANN has been designed and the outputs of the FE simulation of the hot forging are used for training the network and then, the network is employed for prediction of the behavior of the output parameters during the isothermal forging process. Experimental data is compared with the FE predictions to verify the model accuracy. The performance of the ANN model is evaluated using a wide variety of standard statistical indices. Results show that the ANN model can efficiently and accurately predict isothermal hot forging behavior of AlCuMgPb alloy. Finally the extrapolation ability and noise sensitivity of the ANN model are also investigated. It is found that the extrapolation ability is very high in the proximity of the training domain, and the noise tolerance ability very robust. Manuscript profile