PURPOSE: Monitoring appropriate categories of medication sales can provide early warning of certain disease outbreaks. This paper presents a methodology for choosing and monitoring medication sales relevant for the surveillance of gastroenteritis and assesses the operational characteristics of the selected medications for early warning. METHODS: Acute diarrhoea incidences in mainland France were obtained from the Sentinelles network surveillance system for the period 2000-2009. Medication sales grouped by therapeutic classes were obtained on the same period. Hierarchical clustering was used to select therapeutic classes correlating with disease incidence over the period. Alert thresholds were defined for the selected therapeutic classes. Single and multiple voter algorithms were investigated for outbreak detection based on sales crossing the thresholds. Sensitivity and specificity were calculated respective to known outbreaks periods. RESULTS: Four therapeutic classes were found to cluster with acute diarrhoea incidence. The therapeutic class other antiemetic and antinauseants had the best sensitivity (100%) and timeliness (1.625 weeks before official alerts), for a false alarm rate of 5%. Multiple voter algorithm was the most efficient with the rule: 'Emit an outbreak alert when at least three therapeutic classes are over their threshold' (sensitivity 100%, specificity 95%, timeliness 1.750 weeks before official alerts). CONCLUSIONS: The presented method allowed selection of relevant therapeutic classes for surveillance of a specific condition. Multiple voter algorithm based on several therapeutic classes performed slightly better than the best therapeutic class alone, while improving robustness against abrupt changes occurring in a single therapeutic class.