Neuropsychological and neuroimaging evidence suggests that the human brain contains facial expression recognition detectors specialized for specific discrete emotions. However, some human behavioral data suggest that humans recognize expressions as similar and not discrete entities. This latter observation has been taken to indicate that internal representations of facial expressions may be best characterized as varying along continuous underlying dimensions. To examine the potential compatibility of these two views, the present study compared human and support vector machine (SVM) facial expression recognition performance. Separate SVMs were trained to develop fully automatic optimal recognition of one of six basic emotional expressions in real-time with no explicit training on expression similarity. Performance revealed high recognition accuracy for expression prototypes. Without explicit training of similarity detection, magnitude of activation across each emotion-specific SVM captured human judgments of expression similarity. This evidence suggests that combinations of expert classifiers from separate internal neural representations result in similarity judgments between expressions, supporting the appearance of a continuous underlying dimensionality. Further, these data suggest similarity in expression meaning is supported by superficial similarities in expression appearance.