There exist distributed processing environments composed of many heterogeneous computers. It is required to schedule distributed parallel processes in an appropriate manner. For the scheduling, prediction of execution load of a process is effective to exploit resources of environments. We propose long-term load prediction methods with references of properties of processes and of runtime predictions. Since an appropriate prediction method is different according to the situation, we propose a prediction module selection to select an appropriate prediction method according to a state of changing CPU load using a neural network. We also discuss about the implementation of a long-term CPU load prediction system, which provides information including prediction of load for schedulers, system administrators and users.