By the advent of maintenance science, Prognostics and Health Management has gradually penetrated into all the domains of engineering equipment. Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. Costly repairing strategies and Preventive Maintenance are increasingly replaced by modern strategies such as PHM. This strategy, generally, consists of functional modules: data acquisition, data processing, feature extraction/selection, health assessment, diagnosis and prognosis. Although many different methods have been developed in the literature and implemented in different applications for each of the above modules, there is still a need for a comprehensive approach in this matter. This paper attempts to review recent studies in diagnosis and prognostics, as well as data-driven, model-based and hybrid approaches. Finally, the paper provides discussions on current situation of PHM and a roadmap for future trends for researchers in this ﬁeld is presented.