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Rangelands in Sudan constitute the main land use system in arid and semi-arid areas, that provides good sources for feeding huge numbers of animals in addition to their environmental and social importance, therefore, these areas are considered economically and environmentally important for the pastoral sector in the Sudan. This study aims to differentiate between different rangelands sites in semi-arid areas of Sudan using RS for management purposes. The methodology based on using remote sensing techniques including principal component analysis to differentiate between different rangeland sites as indicated by soil types and vegetation patterns for monitoring and assessment purposes. Characteristics of these rangeland sites were used to understand soil/plant/landform relationships and their response to ecological and management considerations. Three rangeland sites were identified in the study area as gardud, sand sheet and sand dune. Principal Component Analysis (PCA) was used to reduce the number of factors needed to distinguish between rangeland sites where new data set including the most useful spectral information for differentiation to run image processing and automated classification. The selected types of data were used (two vegetation indices, topographic data and vegetation surface reflectance within the three bands of MODIS data).Un-supervised classifications were applied to the new dataset. The method categorized the image into three classes for the two years 2005 and 2011. Analysis with PCA indicated that there is a relatively high correspondence between vegetation and soil of the total variance in the data set. The results showed that the use of the principal component analysis with the selected variables showed high differences that can be used for differentiation. The results also showed that the use of PCA to make unsupervised classification with these variables, cross-checked with ground points enabled identification of main vegetation groups within the sites, which is a more refined reference for monitoring.