We report research on the assessment of Boolean minimization in symbolic empirical learning. We view training examples as logical expressions and implement Boolean Minimization (BM) heuristics to optimize input and to learn symbolic knowledge rules. We base our work on a B M learning system called B M L. B M L includes three components : a preprocessing, a B M, and a postprocessing component. The system incorporates Espresso-II, a popular system in very large scale integration design. The preprocessing and postprocessing components include utilities that support preparation of training exampleson the one hand and assessment of learned output on the other. We test B M L using 10 different domains and compare performance with C4 . 5 , AQ15 , NewId , and CN2 using classification accuracy and rule quality statistics. We conclude by reviewing results and by discussing areas for future research .