New approach for classification of high-resolution satellite images is presented in the article. That approach has been developed at the Institute of Geodesy and Cartography, Warsaw, within the Geoland 2 project – SATChMo Core Mapping Service. Classification algorithm, aimed at recognition of generic land cover categories, has been elaborated using the object-oriented approach. Its functionality was tested on the basis of KOMPSAT-2 satellite images, recorded in four multispectral bands (4 m ground resolution) and in panchromatic mode (1 m ground resolution). The structure of the algorithm resembles decision tree and consists of a sequence of processes. The main assumption of the presented approach is to divide image contents into objects characterized by high and low texture measures. The texture measures are generated on the basis of a panchromatic image transformed by Sigma filters. Objects belonging to the so-called high texture are classified at first steps. In the following steps the classification of the remaining objects takes place. Applying parametric criteria of recognition at the first group of objects four generic land cover classes are classified: forests, sparse woody vegetation, urban / artificial areas and bare ground. Non-classified areas are automatically assigned to the second group of objects, which contains water and agricultural land. In the course of classification process a few segmentations are performed, which are dedicated to particular land cover categories. Classified objects, smaller than 0.25 ha are removed in the process of generalization.