Imaging spectrometers have the potential to identify surface mineralogy based on the unique absorption features in pixel spectra. A back-propagation neural network (BPN) is introduced to classify Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) of the Cuprite mining district (Nevada) data into mineral maps. The results are compared with the traditional acquired surface mineralogy maps from spectral angle mapping (SAM). There is no misclassification for the training set in the case of BPN; however 17percent misclassification occurs in SAM. The validation accuracy of the SAM is 69percent, whereas BPN results in 86percent accuracy. The calibration accuracy of the BPN is higher than that of the SAM, suggesting that the training process of BPN is better than that of the SAM. Thehigh classification accuracyobtained withthe BPN can beexplained by: (1) its ability to deal with complex relationships (e.g., 40 dimensions) and (2) the nature of the dataset, the minerals are highly concentrated and they are mostly represented by pure pixels. This paper demonstrates that BPN has superior classification ability when applied to imaging spectrometer data.