Machine tool characteristics such as machine vibration and stiffness have a large influence on cutting processes. Cutting processes, therefore, are dependent on machine tool characteristics as well as the characteristics of cutting tool and workpiece. This paper describes autonomous operation planning that can optimize cutting operation with evaluating machine tool characteristics. Machine tool characteristics that have an effect on tool wear and surface finish are evaluated by adaptive prediction. Adaptive prediction, which can predict cutting processes by analysis and neural network, evaluates the characteristics with the parameters used for the predictions. Adaptive prediction is carried out exclusively on two machine tools, and then it enables us to recognize the difference of machine tool characteristics. Autonomous operation planning with adaptive prediction allows us to determine optimum cutting conditions for each machine tool. This leads to the reasonable usage of machine tool in shop floor. It is shown that machining scheduling with autonomous operation planning is efficient in the assignment of six jobs to two machine tools.