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The relationship between emotions and learning was investigated by tracking the affective states that college students experienced while interacting with AutoTutor, an intelligent tutoring system with conversational dialogue. An emotionally responsive tutor would presumably facilitate learning, but this would only occur if learner emotions can be accurately identified. After a learning session with AutoTutor, the affective states of the learner were classified by the learner, a peer, and judges trained on Ekman’s Facial Action Coding system. The classification of the trained judges was more reliable and matched the learners much better than the low scores of untrained peers. This result suggests that peer tutors may be limited in detecting the affective states of peer learners. Classification accuracy was poor at constant intervals of polling (every 20 seconds) but much higher when individuals declared that an affect state had been experienced.