Many of today's visual scene and object categorization systems learn to classify using a statistical profile over a large number of small-scale local features sampled from the image. While some application systems have been constructed, this technology has enjoyed far more success in the research setting. The approach is best suited to tasks where within-class variability is small compared to between-class variability. This condition holds for large diverse artificial collections such as CalTech 101 where most categories have little to do with each other, but it often does not hold among naturalistic application-driven categories. Here, category distinctions are more likely to be conceptual or functional, and within-class differences can rival or exceed between-class differences. In this paper, we show how the local feature approach can be extended using explanation-based learning (EBL). The EBL approach makes use of readily available prior domain knowledge assembled into plausible explanations for why a training example's observable features might merit its assigned training label. Explanations expose additional semantic features and suggest how those hidden features may be estimated from observable features. We exhibit our approach on two CalTech 101 dataset tasks that we argue are emblematic of applied domains: Ketch vs. Schooner and Airplane vs. Background. In both cases classification accuracy is significantly improved.