An investigation of rule learning processes that allow the inclusion of negated features is described. The objective is to establish whether the use of negation in inductive rule learning systems is effective with respect to classification. This paper seeks to answer this question by considering two issues relevant to such systems; feature identification and rule refinement. Both synthetic and real datasets are used to illustrate solutions to the identified issues and to demonstrate that the use of negative features in inductive rule learning systems is indeed beneficial.