Condition monitoring, as part of the intelligent infrastructure concept, can significantly improve the reliability, safety and efficiency of rail operations. Degradation in infrastructure can be detected before a problem occurs, without interrupting normal operations. This paper contributes to the development of analytics tools and methods for condition monitoring using sensor data. A data-driven method is proposed to monitor the wheel wear of high-speed trains using onboard vibration sensors. In this method, a number of signal processing techniques and statistical methods are applied and extended to extract useful information from multi-location vibration data and estimate the wheel wear. We test the method using real operational data collected from high-speed trains in China over half a year. Preliminary results show that the method is accurate and can be easily applied to real world operations. The method can be extended to provide fault or degradation predictions, which reduces the in-service failures, and enables predictive maintenance.