High variability of object features and bad class separation of objects are the main causes for the difficulties encountered during the interpretation of ground-level natural scenes. For coping with these two problems we propose a method which extracts those regions that can be segmented and immediately recognized with sufficient reliability (core regions) in the first stage, and later try to extend these core regions up to their real object boundaries. The extraction of reliable core regions is generally difficult to achieve. Instead of using fixed sets of features and fixed parameter settings, our method employs multiple local features (including textural features) and multiple parameter settings. Not all available features may yield useful core regions, but those core regions that are extracted from these multiple features make a cntributio to the reliability of the objects they represent. The extraction mechanism computes multiple segmentations of the same object from these multiple features and parameter settings, because it is not possible to extract such regions uniquely. Then those regions are extracted which satisfy the constraints given by knowledge about the objects (shape, location, orientation, spatial relationships). Several spatially overlapping regions are combined. Combined regions obtained for several features are integrated to form core regions for the given object calss.