Abstract: The Prognostic Health Monitoring (PHM) of milling cutters is key issue in the field of machine tool manufacturing. As the "teeth" of computer numerical control (CNC) machine tools, its health status directly affects the machining efficiency and the quality of products. It has become a hot topic in both academic research and industries, with the help of big data and deep learning technology to realize high reliable tool fault diagnosis and predictive maintenance. However, the lack of high-quality yet life-cycle data has become the bottleneck restricting academic research and engineering application. To solve this problem, the end milling cutter life test in CNC machining center are carried out, and the obtained test data set are published for scholars all over the world. This data set contains the vibration signals of the end milling cutter in the whole life cycle under two working conditions, and clearly marks five groups of data, such as the maximum wear width at the main back of the cutter, wear area S_VB and wear width VB at 1/2a_p (back feed), and the maximum wear width and wear area S_VB at the auxiliary back, which provides data support for the research in PHM field.