decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification tasks. Nevertheless, there are still a lot of problems especially when dealing with numerical (continuous valued) attributes. Some of those problems can be solved using fuzzy decision trees (FDT). Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, a few researchers independently have proposed to utilize fuzzy representation in decision trees to deal with similar situations. Fuzzy representation bridges the gap between symbolic and non symbolic data by linking qualitative linguistic terms with quantitative data. In this paper, a new method of fuzzy decision trees is presented. This method proposed a new method for handling continuous valued attributes with user defined membership. The results of crisp and fuzzy decision trees are compared at the end.