Asthma and COPD (Chronic Obstructive Pulmonary Disease) are obstructive respiratory diseases that affect negatively the quality of life for patients and their families worldwide. Despite the significance of these diseases, their management has been considered suboptimal around the world, whereas improper inhaler use has been underlined as one of the main causes. This work presents an integrated mHealth system that provides real-time personalized feedback to patients for assessing proper medication use. The identification of proper inhaler use is based on data-driven feature extraction and classification methods employed for the identification of four events (inhaler actuation, inhalation, exhalation, background noise). The proposed scheme reaches 98% classification accuracy.