This paper proposes SymDetector, a smartphone based appli- cation to unobtrusively detect the sound-related respiratory symptoms occurred in a user’s daily life, including sneeze, cough, sniffle and throat clearing. SymDetector uses the built-in microphone on the smartphone to continuously mo- nitor a user’s acoustic data and uses multi-level processes to detect and classify the respiratory symptoms. Several prac- tical issues are considered in developing SymDetector, such as users’ privacy concerns about their acoustic data, resource constraints of the smartphone and different contexts of the smartphone. We have implemented SymDetector on Galaxy S3 and evaluated its performance in real experiments involv- ing 16 users and 204 days. The experimental results show that SymDetector can detect these four types of respiratory symptoms with high accuracy under various conditions.