The methods that were employed in this project analyzed HIV data. The aim was to evaluate the evolution of HIV positive patients to bring out some significant factors associated with this pathology. Many clinical situations can be described in terms of the conditions that individuals can be in (states), how they can move among such states (transitions), and how likely such moves are (transition probabilities). State transition models were, therefore, best suited to analyze this decision problem. Transition probabilities from states 1, 2, and 3 into state 4 increased as time progressed. The estimated total length of stay in state 1 was longer than state 2 and 3 respectively.