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In machining technologies, the most important criterion taken into consideration when evaluating the product quality is seen as the surface roughness. In the consideration of production quality and cost, tool wear is one of the factors that directly affect the cost of production. In the machining process, the most important parameters affecting the surface roughness and tool temperature are the cutting depth, speed and feed rate of rotation. In order to obtain the best surface quality and to keep the cost at the optimum level, the most suitable processing parameters should be selected by taking into consideration the effect of these parameters on each other. In this study, it is aimed that to prediction of surface roughness (Ra.) and tool temperature (°C) values for turning which has an important position in machining. For this purpose, Artificial Neural Networks (ANN) method and Multi Linear Regression Model (MLRM) were used separately. The data obtained from ANN, Regression Model were compared with the actual test data, and the results were examined. According to the obtained results, it is seen that the ANN method has more successful results than Regression model in surface roughness and tool temperature estimation.