This paper presents a predictive maintenance framework for CNC machines focusing on a multi-stage prediction of machines’ health status. For the implementation of such a multi-stage prediction, the proposed approach includes two prediction layers: the cloud prediction layer and the local prediction layer. Each layer provides a prediction of machine health status in different timescale. The local prediction layer, based on data analysis techniques, is responsible to predict the health status of the machine for a short time period. Thus, this prediction can be used as an alarm aiming to prevent un-expected breakdowns. The cloud prediction layer, based on digital physical-based models, is responsible to provide a more general overview of machine health status using Prognostics and Health Management (PHM) techniques, useful for long timespan strategies definition. This paper presents the proposed approach and its benefits are described and discussed. The proposed approach will be implemented in the PROGRAMS project.