Extraction of concepts and identification of their seman- tic classes are useful in applications such as automatic in- stantiation of ontologies and construction of information extraction systems. Even though various techniques exist for the extraction of domain specific concepts from unstruc- tured texts, very little concentration is in the semantic class labeling for concepts. In this paper we propose the Seman- tic Class Labeling(SCL) problem and differentiate it from the Named Entity Classification(NEC) problem. We also present a Naive Bayes solution to SCL. Experiments suggest that Naive Bayes learning method with specified features achieves high classification accuracy. Empirical and statis- tical evaluation on the significance of attributes for SCL is also presented. keywords: Concept classification, Naive Bayes Classi- fier, Text Mining.