We present an automatic semantic roles labeling system for structured trees of Chinese sentences. It adopts dependency decision making and example-based approaches. The training data and extracted examples are from the Sinica Treebank, which is a Chinese Treebank with semantic role semantic roles including thematic roles, such as ‘agent’; ‘theme’, ‘instrument’, and secondary roles of ‘location’, ‘time’, ‘manner ’ and roles for nominal modifiers. The design of role assignment algorithm is based on the different decision features, such as head-argument/modifier, case makers, sentence structures etc. It labels semantic roles of parsed sentences. Therefore the practical performance of the system depends on a good parser which labels the right structures of sentences. The system achieves 92.71 % accuracy in labeling the semantic roles for pre-structure- bracketed texts which is considerably higher than the simple method using probabilistic model of head-modifier relations. 1.