Hyperspectral remote sensing combined with advanced image processing techniques is an efficient tool for the identification of agricultural crops. In our study we pursued spectral analysis on a relatively small sample area using low number of training points to examine the potential of high resolution imagery. Spectral separability measurements were applied to reveal spectral overlapping between 4 crop species and for the discrimination we also used statistical comparisons such as plotting the PC values and calculating standard deviation of single band reflectance values on our classes. These statistical results were proven to be good indicators of spectral similarity and potential confusion of data samples. The classification of Spectral Angle Mapper (SAM) had an overall accuracy of 72% for the four species where the poorest results were obtained from the test points of garlic and sugar beet. Comparing the statistical analyses we concluded that spectral homogeneity does not necessarily have influence on the accuracy of mapping, whereas separability scores strongly correlate with classification results, implying also that preliminary statistical assessments can improve the efficiency of training site selection and provide useful information to specify some technical requirements of airborne hyperspectral surveys.