Physical inactivity significantly impacts personal health, reduces quality of life, and often leads to mobility disorders, diabetes, and cardiovascular disease. Monitoring daily life activities by means of wearable inertial sensors can provide valuable feedback necessary to improve the quality of daily life and prevent the development of mobility disorders caused by physical inactivity. In this study, a physical activity classification (PAC) algorithm was developed and tested using an inertial sensor-based dataset. The dataset was acquired from multiple inertial sensors, each mounted at a different body location, and consists of various Activities of Daily Living (ADL). Data from nineteen healthy young subjects were analyzed. Time- and frequency-domain features from raw 3D accelerometer and 3D gyroscope signals were computed by performing windowing of the time series data. The K-nearest neighbors (KNN) pattern recognition algorithm was used to classify thirteen different ADLs and was evaluated by a 10-fold cross-validation. The proposed PAC algorithm outperformed the existing algorithm validated using the same dataset, with an overall mean classification rate (sensitivity) of 97.38%. This paper discusses the limitations of this study and proposes ways to overcome said limitations in order to make the PAC algorithm more effective in real-life conditions.