We investigate how the granularity of POS tags influences POS tagging, and furthermore, how POS tagging performance relates to parsing results.For this, we use the standard "pipeline" approach, in which a parser builds its output on previously tagged input.The experiments are performed on two German treebanks, using three POS tagsets of different granularity, and six different POS taggers, together with the Berkeley parser.Our findings show that less granularity of the POS tagset leads to better tagging results.However, both too coarse-grained and too fine-grained distinctions on POS level decrease parsing performance.