Generating fuzzy rules from training data is the most vital assignment in design of fuzzy classification system. In this paper, we present an approach to deal with the classification problem where fuzzy logic is used. We intend to show that fuzzy logic introduces new elements in the identification process, to manage imprecise information. A method to generate set of definitive fuzzy rules from initial training data is introduced. A triangular membership function is used for generating fuzzy rules from training data as they are simpler and more human understandable with high interpretability.
