Railcar asymmetric wheel wear leads to severe wear on one wheel but mild wear on the other wheel. The consequences of the asymmetric wheel include accelerated wear, mechanical failure and downtime, and high financial penalties. Therefore, identifying the asymmetric wheel wear is critical not only for cost effective maintenance but also for safe operations. Fortunately, the increasing amount of various wayside detectors is instrumented along the railway that can monitor the health of railcar components and log plenty of detailed information about railroad operations. One can use this information to identify the asymmetric wheel wear in the early stage. However, most elliptically contoured distributions are effective in describing normal events but not in dealing with the outliers, which mainly locate in the tails of the distribution. Asymmetric wheel wear requires effective anomaly detection that mainly focuses on the extreme values in the tail of a right-skewed distribution. In this paper, we employ the Extreme Value Theory (EVT), which handles the unusually high or low data in the distribution, to derive an extreme value score to identify asymmetric wheel wear. Experiment results show that identification of asymmetric wheel wear can generate huge monetary benefit in terms of reducing average maintenance times of railcars.