Chronic headache sufferers use headache diaries to learn about their headache symptoms and triggers. But the existing headache diaries do not support identification of probable headache triggers which is a critical requirement for self-monitoring of headaches. The literature describes several applications that keep track of headaches, but none of them allow the patients to identify potential headache triggers by exploring the correlations between the self-tracked factors and the onset of headaches. In this thesis, a self-monitoring application is designed that supports reviewing of headache trends and enables interactive visual exploration of potential correlations between the headaches and the putative triggers based on temporal data analysis. The design of the application reflects the data collection and the analytical needs of the headache patients. The evaluation results suggest that the application can be useful for the headache patients to identify their potential headache triggers, and hence enable better self-monitoring of headaches.