Category learning theorists tacitly assume that stimuli are encoded by a single pathway. Motivated by theories of object recognition, we evaluated a dual-pathway account of stimulus encoding. The part-based pathway establishes mappings between sensory input and symbols that encode discrete stimulus features, whereas the image-based pathway applies holistic templates to sensory input. Our experiments used rule-plus-exception structures, in which one exception item in each category violates a salient regularity and must be distinguished from other items. In Experiment 1, we found discrete representations to be crucial for recognition of exceptions following brief training. Experiments 2 and 3 involved multisession training regimens designed to encourage either part- or image-based encoding. We found that both pathways are able to support exception encoding, but have unique characteristics. We speculate that one advantage of the part-based pathway is the ability to generalize across domains, whereas the image-based pathway provides faster and more effortless recognition.