Abstract: Urban vegetation system has great ecological value to social-economic-natural ecosystem,but it was often submitted to different stresses caused by air pollution, water pollution, "heat island" problem, etc., which debases its ecological service functions, so it is important to develop methods to monitor urban vegetation stress level. Using the Hyperion hyper-spectral data, which has advantage in monitoring vegetation physiological characters on large scale, an urban vegetation stress level monitoring method was developed based on vegetation stress feature selection and RBFNN (Radial Basis Function Neural Network). Firstly, 14 hyperspectral vegetation indices were extracted from reflectance image of Hyperion and a feature selection based on correlation analysis was conducted to reduce the redundancies. Then an urban vegetation stress level classifier based on RBFNN was trained on the selected features and vegetation stress level was classified and mapped. Finally, the spatial distribution characteristics of vegetation stress in urban and some reasons were analyzed. The result shows that the RBFNN vegetation stress level classifier is able to identify vegetation stress level quickly and accurately. Vegetation stress level is correlated largely with urban traffic pollution and human disturbance, and vegetation in commercial and residential areas of urban center are apparently experiencing higher stress than vegetation in suburban regions; the stress level shows a ringy distribution around large greenbelts.