Chatter vibration is a condition which hinders effective performance of material removal in machining operations. This kind of vibration is dangerous and leads to over-vibration between workpiece and tool. Additionally, it results in low surface quality, loudness and excessive tool wear. In order to prevent the chatter vibration, there are different methods in the literature by which vibration can be effectively controlled. The aim of this study is to determine the optimum parameters of chatter vibrations in turning process and develop a hybrid decision-making algorithm which consists of artificial neural networks-TOPSIS methods for the optimization of machining parameters. First, stable cutting depths, chatter frequencies and other modal parameters are determined by an empirical study. Then, a new hybrid decision-making model is developed and optimum machining parameters are determined. It is observed that the hybrid decision-making model produces successful results and chatter vibrations are prevented.