Manufacturing processes consist of activities affected by a large number of variables. The aim of this study is to show that improvements can be made by using artificial neural network methods at stages of manufacturing such as planning of processes, forecasting of the future situation, monitoring and control. In the study, a manufacturing process with 15 input variables was modeled using artificial neural networks, network training was provided, and a trained network was used to obtain the best output performance in the current situation. Artificial neural networks are useful tools in finding out the consequences of any change that may occur in variables and in improving the processes with this way. The results show that artificial neural network models can be well adapted to manufacturing processes.