Most decision tree induction methods used for extracting knowledge in classification problems are unable to deal with uncertainties embedded within the data, associated with human thinking and perception. This thesis describes the development of a novel tree induction algorithm which improves the classification accuracy of decision trees in non-deterministic domains. A novel algorithm, Fuzzy CIA, is presented which applies the principles of fuzzy theory to decision tree algorithms in order to soften the sharp decision boundaries which are inherent in these induction techniques. Fuzzy CIA extrapolates rules from a crisply induced tree, fuzzifies the decision nodes and combines membership grades using fuzzy inference. A novel approach is also proposed to manage the defuzzification of regression trees. The application of fuzzy logic to decision trees can represent classification of knowledge more naturally and in-line with human thinking and creates more robust trees when it comes to handling imprecise, missing or conflicting information. A series of experiments, using real world datasets, were performed to compare the performance of Fuzzy CIA with crisp trees. The results have shown that Fuzzy CIA can significantly improve the classification / prediction performance when compared to crisp trees. The amount of improvement is found to be dependant upon the data domain, the method in which fuzzification is applied and the fuzzy inference technique used to combine information from the tree.