This study presents an example of multi-spectral classification mapping, in the Ibex Hills, CA , USA, which was used to support field endeavors, the results of which were then compared to recent geologic mapping in order to assess the classification results. To do this a combination of ENVI and QGIS softwares, using bands 1-9 of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multi-spectral data, were used to create supervised classification maps. The inputs into the supervised classifications were combinations of Principal Component Analysis (PCA) of the ASTER bands and ASTER band ratios. The results herein showed that utilizing a combination of both PCA and band ratios for classification resulted in the most accurate results when compared to the training data. Two different classification algorithms were compared, Parallelepiped and Maximum Likelihood (ML), which both performed at a similar overall accuracy, ~54%, but varied in effectiveness with different lithologies with the Parallelepiped excelling at gneiss and mafic intrusive units, at ~85%+ accuracy, and Maximum Likelihood excelling at identifying shales, at ~72% accuracy. While issues remain with classifying certain lithologies the classification maps provided a useful, albeit coarse, tool for field work. Although the methodologies of this study are not original, the heterogeneity inherent in geologic field sites can result in a variety of spectral signatures for similar lithologies, thus making case-studies of specific regions an important tool for future work in those areas.