This paper investigates the ability to classify different vegetation types covering a semi-arid Mediterranean vegetated area using single-polarization SAR images from the TerraSAR-X (TSX-1) satellite. Based on statistical moments such as mean, standard deviation (STDEV), skewness and kurtosis, we found textural differences useful for the classification of the vegetation types. The research site, located near Zafit Hill, Israel, includes several different vegetation types such as pines and cypress forests with shrubs as an underlying vegetation layer (understory), olive orchards, eucalyptus clusters, natural grove areas with the presence of stones and smooth rocks, a wet cotton field, and smooth agricultural fields after harvest. In each vegetation type area, 40 equal polygons (10*10 pixels each) were identified on an optical image and defined on the TSX-1 image; 280 polygons in total were identified. The aforementioned statistical parameters were produced for each polygon, and co-variance matrices of combinations of two, three, or all four parameters together were produced. It was found that using the Mahalanobis distance of the mean-STDEV-skewness combination after applying a mode filter (5 *5 in size) was the best way to classify the vegetation types in the research area.