Abstract: OBJECTIVES: Application of Association rules in data mining is becoming increasingly popular. This is a technique of identifying some strong patterns and using it for finding correlations between attributes from large data repositories. For the serious consequences that the drug adverse reactions can have (both at micro and macro level), in this paper, Association Rule Mining has been implemented to AERS (Adverse Event Reporting System) quarterly data extracts maintained by FDA (Food and Drug Administration), a US federal unit, to identify  some strong association between drugs and associated reactions. Paper also throws light on some other popular databases where the technique could be applied to find useful patterns and report on the rules generated. METHODS: Study involved extracting useful information from the quarterly tables produced, synthesizing the information to get the rules using Apriori algorithm varying the confidence[1]  and other measure levels. Interactions of Patients’ demographic characteristics (like age, gender, etc.), length of therapy, dosages with drugs taken were also explored to see if such factors play a role in driving the reactions. RESULTS: