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For received signal strength (RSS) fingerprint based indoor localization approach-es, the localization accuracy is significantly influenced by the RSS variance, device hetero-geneity and environment complexity. In this work, we present a high-adaptability indoor localization (HAIL) approach, which leverag-es the advantages of both relative RSS values and absolute RSS values to achieve robustness and accuracy. Particularly, a backpropagation neural network (BPNN) is devised in HAIL to measure the fingerprints similarities based on absolute RSS values. With this aid, the charac-teristics of the applied area could be specially leed such that HAIL could be adaptive to different environments. The experiments demonstrate that HAIL achieves high local-ization accuracy with the average localization error of 0.87m in the typical environments. Moreover, HAIL has the minimum amount of large errors and decreases the average local-ization error by about 30％～50％ compared with the existing approaches.