In recent years, railways play a vital role in freight and passenger transport. As safety-critical systems, it is crucial to accurately detect and predict, as early as possible, any faults that may affect their operation. In this context, prognostic and health management (PHM) offers promising opportunities for the design and implementation of effective predictive maintenance strategy. To allow successful development of PHM in railway industry, it is important to study its key enabling technologies. Hence, we provide in this paper an overview of fundamental pillars for PHM deployment. This includes condition monitoring technologies, namely sensing and networking techniques. Besides, powerful methods for robust and accurate data processing based on artificial intelligence are presented. Moreover, we highlight barriers in railway PHM development, which should be addressed by both the research community and the industry.