Dissertation deals with the accuracy of the classification of selected tree species from hyperspectral materials. Fagus sylvatica L., Quercus sp., Carpinus betulus L., Picea Abies l., Pinus sylvestris L. and Abies alba Mill., have a significant percentage of representation within the tree species composition in Slovakia. Data from two sources were used for classification. The first source was the spectral profiles of bark and assimilation organs, which were measured in the laboratory conditions by spectroradiometer LI-COR li-1800. A spectral range of electromagnetic radiation ranging from 300 to 1100 nm at a spectral resolution of 2 NM was recorded from measurement. The second source was the spectral profiles of the assimilation organs derived from the air hyperspectral materials captured by the AISA Eagle sensor, which recorded a range from 400 to 1000 nm at a spectral resolution of 10 nm. Each sets of hyperspectral materials have been subjected to discriminatory analysis, through which spectra have been selected, which have the largest discriminatory strength. The overall accuracy of the classification for the data measured in the laboratory conditions has reached 82.81 % (the maximum likelihood method), 64.06% (SAM method), and assimilation organs 90.32% (maximum assurance method), 70.97% ( SAM method). In the spectral classification of the image of hyperspectral records from AISA Eagle, overall accuracy was achieved with the method of maximum assurance 84.93%, and with the SAM method 65.96%. The results of the work demonstrate the usability of the spectral properties of the analyzed surfaces, which could be used in the classification of tree species in combination with other appropriate technologies for the geospatial data collection (ground scanning, near photogrammetry ,...). The outputs of the investigated technologies could form an essential information base for the needs of precise forestry.