Abstract: The recognition and extraction of mine tailing information serve as an important step in the dynamic monitoring of the mine environment. The classification of surface features using medium-low spatial resolution images is mostly conducted based on spectral information. However, some roads and tailings have similar spectral reflectance due to the special environment in mining areas. As a result, it is liable to misclassify tailings as roads in the surface feature classification based on spectral information only, which affects the structural integrity and area statistics of tailing ponds. Given this, this paper comprehensively analyzes the spectral, shape-related, and texture characteristics of iron mine tailings in the Qianxi area, Hebei Province based on high spatial resolution images obtained from the Beijing-2 satellite and proposes an object-oriented classification method based on multiple features. The steps of the method are as follows. Firstly, perform multi-scale segmentation of Beijing-2 images and the reflectance and take the spectral differences of surface features in each band as the spectral characteristic values of surface features. Secondly, extract the values of length-to-width ratio of objects using a covariance matrix and object boundaries and take them as the characteristic values of surface feature shapes. Then, calculate the gray-level co-occurrence matrix using principal component bands, and select the contrast, correlation, and entropy values that can effectively distinguish the texture characteristics between tailings and other surface features as the texture characteristic values of remote sensing images. Finally, conduct object-oriented classification and precision assessment using the nearest neighbor method according to the characteristic information of surface features. The results indicate that the object-oriented classification method can effectively avoid the misclassification of the roads in tailing ponds and thus provide a research basis for the implementation of large-scope and high-precision identification and dynamic monitoring of mine tailings.