This paper presents Chinese Part-of-speech (POS) tagging using maximum entropy technique, in which we introduce a novel gain-driven method for feature selec- tion, then we describe the restricted training method for model learning. We test our approach on the simpli¯ed Chinese corpus of Peking University China and achieve an accuracy of 97.80% and 98.60% over ¯ne and coarse grained tag set - a signi¯cant improvement over the existing Chi- nese POS tagger.