In treating asthma and chronic obstructive pulmonary disorder (COPD), acquisition of authentic and effective feedback from patients on regimen adherence is difficult. Face-to-face and oral reporting methods do not satisfy current intelligent medication best practices. This paper presents a system to track and analyse daily inhaler usage. A portable electronic device that attaches to the inhaler uses an accelerometer and capacitive sensors to detect users' motion and an embedded digital microphone to capture sounds while the inhaler is in use. In terms of analysis, sound features are extracted, and breath phases are identified by employing a hidden Markov model (HMM) with a Gaussian mixture model (GMM). A feature template is also constructed and used to search for and identify 'canister pressed' events. The system provides objective feedback, quantifying asthma and COPD patients' adherence to medication regimens. Although interest in asthma adherence to medication regimens is growing, there is still a relative paucity of research and, indeed, compliance devices in this area; the tracking system can help doctors better understand the patient's condition and choose an appropriated treatment plan. At the same time, patients can also improve their self-management by system feedback.