Background Certain chronic diseases such as migraine result in episodic, debilitating attacks for which neither cause nor timing is well understood. Historically, possible triggers were identified through analysis of aggregated data from populations of patients. However, triggers common in populations may not be wholly responsible for an individual's attacks. To explore this hypothesis we developed a method to identify individual potential trigger' profiles and analysed the degree of inter-individual variation. Methods We applied N=1 statistical analysis to a 326-migraine-patient database from a study in which patients used paper-based diaries for 90 days to track 33 factors (potential triggers or premonitory symptoms) associated with their migraine attacks. For each patient, univariate associations between factors and migraine events were analysed using Cox proportional hazards models. Results We generated individual factor-attack association profiles for 87% of the patients. The average number of factors associated with attacks was four per patient: Factor profiles were highly individual and were unique in 85% of patients with at least one identified association. Conclusion Accurate identification of individual factor-attack profiles is a prerequisite for testing which are true triggers and for development of trigger avoidance or desensitisation strategies. Our methodology represents a necessary development toward this goal.