The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize human aaective state given four physiological signals. We describe diicult issues unique to obtaining reliable aaective data, and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-today variations: the features of diierent emotions on the same day tend to cluster more tightly than do the features of the same emotion on diierent days. To handle the daily variations, we propose new features and algorithms and compare their performance. We nd that the technique of seeding a Fisher Projection with the results of Sequential Floating Forward Search improves the performance of the Fisher Projection, and provides the highest recognition rates reported to date for classiication of aaect from physiology: 81% recognition accuracy on eight classes of emotion, including neutral.