Based on the study of literature on the classification of forests using high resolution space images it was established that the separation of classes and classes close to the spectral brightness can not be identified with high accuracy. Classification using maximum likelihood algorithm, which generally gives better results compared with algorithms of spectral distance or Mahalanobis distance, does not lead to the definition of areas with a high probability. Therefore, the article examines a method of forest classification by post-processing. Experimental studies were car- ried out using an satellite image of the forested area of the Precarpathian region, obtained from QuickBird-2 (June 2010). Data collected during field research were used as verification data to determine areas of different objects. The controlled classification has been performed using the method of the maximum likelihood, size of signatures for 8 classes were selected from 100 to 400 points. For these classes a matrix of classes separation was calculated, and a significant correlation between next classes was found: young conifer plantings and pine and mixed forest, and deciduous young plantings and deciduous forest. Post-processing significantly improves the reliability of determination of area, and the procedure consists in assigning to all pixel of the selected neighbourhood brightness of most points, although reliability of determination of area depends on the size of the area. Accuracy of determination of areas are from 92 to 99%.