Chronic respiratory diseases may be controlled through the delivery of medication to the airways and lungs using an inhaler. However, adherence to correct inhaler technique is poor, which impedes patients from receiving maximum clinical benefit from their medication. In this study, the Inhaler Compliance Assessment device was employed to record audio of patients using a Diskus dry powder inhaler. An algorithm that classifies inhaler sounds (blister, inhalation, interference) was developed to automatically assess patient adherence from these inhaler audio recordings. The presented algorithm employed audio-based signal processing methods and statistical modeling in the form of quadratic discriminant analysis (QDA). A total of 350 audio recordings were obtained from 70 patients. The acquired audio dataset was split evenly for training and testing. A total accuracy of 85.35% was obtained (testing dataset) for this 3-class classification system. A sensitivity of 89.22% and 70% was obtained for inhalation and blister detection respectively. This approach may have significant clinical impact by providing healthcare professionals with an efficient, objective method of monitoring patient adherence to inhaler treatment.