Past research has shown that when tree-structured back- ground knowledge is available, it can be exploited to increase the efﬁciency of classiﬁcation learning. When this kind of background knowledge is available, the problem becomes one of compositional classiﬁcation. Of course, if the back- ground knowledge contains errors, the quality of the learned hypothesis will suffer. In this paper we study the effect of faulty knowledge engineering on compositional classiﬁcation learning. We present and analyze empirical results that show the impact on the quality of compositional classiﬁcation learning as the quality of knowledge engineering is degraded.