Hyperspectral remote sensing has been widely used in mineral identification using the particularly useful short-wave infrared (SWIR) wavelengths (1.0 to 2.5 μm). Current mineral mapping methods are easily limited by the sensor’s radiometric sensitivity and atmospheric effects. Therefore, a simple mineral mapping algorithm (SMMA) based on the combined application with multitype diagnostic SWIR absorption features for hyperspectral data is proposed. A total of nine absorption features are calculated, respectively, from the airborne visible/infrared imaging spectrometer data, the Hyperion hyperspectral data, and the ground reference spectra data collected from the United States Geological Survey (USGS) spectral library. Based on spectral analysis and statistics, a mineral mapping decision-tree model for the Cuprite mining district in Nevada, USA, is constructed. Then, the SMMA algorithm is used to perform mineral mapping experiments. The mineral map from the USGS (USGS map) in the Cuprite area is selected for validation purposes. Results showed that the SMMA algorithm is able to identify most minerals with high coincidence with USGS map results. Compared with Hyperion data (overall accuracy=74.54%), AVIRIS data showed overall better mineral mapping results (overall accuracy=94.82%) due to low signal-to-noise ratio and high spatial resolution.