Circular scanning of images enables simple feature extraction basically insensitive to object orientation and position. Such is a histogram representing concentric luminous intensity of the image. The histogram shows luminous intensity of concentrinc rings having center in object's centroid. However, features extracted from the image in such a way do not uniquely describe an object. This can cause certain difficulties in pattern recognition stage of image analysis. Latin capitals have been chosen as the objects to be recognized. Two approaches have been analysed. In the deterministic approach pattern recognition is based on matching of the features of an object with those from the feature space. Satisfactory matching results in recognition of the particular capital. Small changes in shapes of the capitals to be recognized can show applicability of the described method in practice. The other approach is based on simple form of adaptive recognition. The result of recognition changes the feature space in order to increase reliability of further recognition. Averaging the data from feature space with the features extracted from the recognized image increase the reliability of further recognition. Experimental results were obtained in problem of recognition of the six capitals given in two different fonts and scales. Images of the capitals were obtained by means of binary semiconductor camera. Image processing, feature extraction and pattern recognition were performed on IBM PC AT.