The paper describes a pattern recognition method based on syntactic image analysis applicable in autonomous systems of robot vision for the purpose of pattern detection or classification. The discrimination of syntactic elements is realized by polygonal approximation of contours employing a very fast algorithm based upon coding, local pixel logic and methods of choice instead of numerical methods. Semantic information is derived from attributes calculated from the filtered shape vector. No a priori information on image objects is required, and the choice of starting point is determined by finding the significant directions on the shape vector. The radius of recognition sphere is minimum Euclidian distance, i.e. maximum similarity between the unknown model and each individual grammar created in the learning phase. By keeping information on derivations of individual syntactic elements, an alternative of parsing recognition is left. The analysis is very flexible, and permits the recognition of highly distorted or even partially visible objects. The output from syntactic analyzer is the measure of irregularity, and the method is thus applicable in any application where sample deformation is being examined.