Application of remote sensing (RS) and geographical information system (GIS) techniques has been increased in natural sciences. In fact, it is inevitable applying of these techniques in vegetation studies due to the existence of some problems in traditional methods (e.g. sampling, calculation, analysis and so on). On this scope, scientists must have sufficient information about the efficiency of these techniques as a useful tool in their studies. This study aims to evaluate the IRS-P6 LISS III and Landsat ETM⁺ efficiency in plant groups’ identification. In order to this purpose, 143 training samples were collected from areas that showed homogenous composition of plant species in at least area of 3600 m² (60 × 60 m). Coordinates of these training samples were recorded using a GPS device and transferred to a GIS database. Also, ENVI 4.2 package has used to process and analyze the satellites data. Several methods of processing such as; spectral separability, supervised classification and assessment of classification accuracy were used in order to gain a satisfy evaluation of the data efficiency. The results indicated that net farming of alfalfa and Juniperus polycarpus–Artemisia kopetdaghensisi community have the most separability on the satellite images (1.99 for Landsat and 2 for IRS). Against, the least separabilities on the Landsat data were between Ju. polycarpus–Onobrychis cornuta and Ju. polycarpus–Ar. kopetdaghensis communities (1.57) and between Ju. polycarpus–Ar. kopetdaghensis and Ju. polycarpus–Agropyron intermedium (1.53) on the IRS data. According to these results, it is concluded that the satellite data are somedeal able to identify plant groups when vegetation communities are sufficiently homogenous, abundant and spectrally and ecologically separable.