This paper deals with the presentation of an experimental platform called PRONOSTIA, which enables testing, verifying and validating methods related to bearing health assessment, diagnostic and prognostic. The choice of bearings is justified by the fact that most of failures of rotating machines are related to these components. Therefore, bearings can be considered as critical as their failure significantly decreases availability and security of machines. The main objective of PRONOSTIA is to provide real data related to accelerated degradation of bearings performed under constant and/or variable operating conditions, which are online controlled. The operating conditions are characterized by two sensors: a rotating speed sensor and a force sensor. In PRONOSTIA platform, the bearing's health monitoring is ensured by gathering online two types of signals: temperature and vibration (horizontal and vertical accelerometers). Furthermore, the data are recorded with a specific sampling frequency which allows catching all the frequency spectrum of the bearing during its whole degradation. Finally, the monitoring data provided by the sensors can be used for further processing in order to extract relevant features and continuously assess the health condition of the bearing. During the PHM conference, a "IEEE PHM 2012 Prognostic Challenge" is organized. For this purpose, a web link to the degradation data is provided to the competitors to allow them testing and verifying their prognostic methods. The results of each method can then be evaluated regarding its capability to accurately estimate the remaining useful life of the tested bearings.