We applied inductive classification techniques to data collected in a Boeing plant with the I goal of uncovering possible flaws in the manufacturing process. This application led us to explore two aspects of classical decision tree induction: (1) preprocessing and postprocessing,and (2) brute-force induction. For preprocessing and postprocessing, much of our effort was focused on the preprocessing of raw data to make it suitable for induction and the postprocessing of learned rules to make them useful to factory personnel. For brute-force induction, in contrast with standard methods, which perform a greedy search of the space of decision trees', we formulated an algorithm that conducts an exhaustive, depth-bounded search for accurate predictive rules. We demonstrate the efficacy of our approach with specific examples of learned rules and by quantitative comparisons with decision tree algorithms (C4 and CART).