The purpose of this study is to identify optimal algorithm for emotion classification which classify seven different emotional states (happiness, sadness, anger, fear, disgust, surprise, and stress) using physiological features. Skin temperature, photoplethysmography, electrodermal activity and electrocardiogram are recorded and analyzed as physiological signals. The emotion stimuli used to induce a participant's emotion are evaluated for their suitability and effectiveness. For classification problems of seven emotions, the design involves two main phases. At the first phase, Particle Swarm Optimization selects P % of patterns to be treated as prototypes of seven emotional categories. At the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative elements of the original feature space. The study offers a complete algorithmic framework and demonstrates the effectiveness of the approach for a collection of selected data sets.