Rhododendron anthopogonoides Maxim., a traditional Tibetan ethnodrug, has been used for antitussive, expectorant, antiasthmatic, heat-clearing and detoxicating, stomachic and swelling agent for a long time, besides, it is also commonly used for treating rheumatoid arthritis. And most of them are wild. Thus, in this study, R. anthopogonoides from 13 different regions were identified in the range of 4 000～400 cm-1 by adopting infrared fingerprint (IR) in order to identify the adulterants, regions and quality of this herbal medicine effectively. Results showed that the infrared spectrum of the samples are similar. And the main IR absorption peaks of the samples were identified and assigned. Then, the fingerprint of R. anthopogonoides was established and the characteristic peaks were at 3 404, 2 921, 2 852, 1 734, 1 625, 1 449, 1 374, 1 266, 1 060, 534 cm-1. However, there were still some differences in the number, position and intensity of the characteristic peaks at 1 517, 1 316, 1 161, 825, 779, 594 cm-1. Moreover, the common peak ratio and variant peak ratio dual-indexes sequential were also calculated and established, respectively. In addition, the cluster analysis was used to analyze the fingerprint data by using SPSS software. What’s more, the grouping results of sequential analysis of dual-indexes and cluster analysis were nearly the same although the analysis principle and aspect of the two methods were different. And it was shown that the two methods are reliable and can be used to analyze the differences in regions and quality of the samples. Results also showed that the common peak ratios of the samples are ≥68.75, and the variant peak ratios are ≤27.27. The common peak ratios are higher when the samples grow in the closer regions with the similar climatic conditions and growing environments, while variant peak ratios are higher when the samples grow in the farther regions with the different climatic conditions and growing environments. The results of cluster analysis showed that when the Euclidean distance is 15, the samples can be clustered into three categories, where R2, R3 and R4 are one class, R7, R8, R10, R11 and R12 are another class, and the rest are classified into the last class. When the Euclidean distance is 20, the samplesare divided into two categories, where R2, R3 and R4 are one class, and the rest are classified into the other class. When the Euclidean distance is 25, the samples from 13 regions are grouped together. So, the relationships between the quality of R. anthopogonoides and their origins can be summarized intuitively by combining the results of cluster analysis with the figure of sampling plots’ location made with ArcGIS. To sum up, fingerprint combined with sequential analysis of dual-indexes and cluster analysis provides a new method which is effective and rapid for the identification of R. anthopogonoides with the adulterants, regions and quality.