In this paper, we present techniques aimed at avoiding typical errors of state-of-the-art POS-taggers and at constructing high-quality POS-taggers with extremely low error rates. Such taggers are very helpful, if not even necessary, for many NLP applications organized in a pipeline architecture. The appropriateness of the suggested solutions is demonstrated in several experiments. Although these experiments were performed only with German data, the proposed modular architecture is applicable for many other languages, too.