The Markov Blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov Blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. This paper applies decision tree induction to the task of Markov Blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0's rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian Network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared.