The rough set approach is a mathematical tool for dealing with imprecision, uncertainty, and vagueness in data. Guarded command languages provide logical approaches for representing constrained nondeterminacy in an otherwise deterministic system without incorporating probabilistic elements. Although from dramatically different functional and mathematical origins, both approaches attempt to resolve observed or anticipated discontinuities between specific pre- and post-condition states of a given information system. This paper investigates the use of a guarded command language in the generation of rough data from explicit decision rules, and in the extraction of implicit decision rules from rough experimental data. Based on these findings, rough sets and guarded command languages appear to be compatible and complementary in their approaches to imprecision and uncertainty. As the association between rough sets and guarded command language represents a new and heretofore untested research direction, possible research alternatives are suggested.