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<p><strong>Abstract.</strong> Geological mapping in desert, mountainous or densely vegetated areas are sometimes faced with many constraints. Recently several remote sensing methods are used on ASTER or LANDSAT imagery for making that task easier. The aim of this paper is to evaluate the applicability of some of these methods on Sentinel-2A images. The study, therefore, focuses on a lithological classification using these multispectral images in the south of the Tafilalet basin. To achieve this goal, two L1C level images were used. Decorelation stretch combined with the optimal index factor (OIF) and Minimum Noise Fraction (MNF) were the main improvements used for RGB combination images. The classifiers Spectral Angle Mapper (SAM) and the Maximum Likelihood classifier (MLC) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest drawn geological maps and RGB images of false colour combinations were used to select regions of interest (ROI) as the endmembers to use for these classifiers. Obtained results showed a clear discrimination of the different lithological units of the study area. Classifications evaluation showed that the Maximum likelihood classifier is more accurate with an overall accuracy of 76% and a Kappa coefficient is 0.74. Finally, this study has shown the importance of the use of sentinel-2 multispectral images in geological mapping and has shown that the high spectral resolution of the VNIR and SWIR bands creates a synergy with the high spatial resolution for optimal lithological mapping.</p>