Abstract-This paper presents an approach of applying principal component analysis (PCA) and fuzzy inference system (FIS) to recognition of activity of daily life (ADL). To overcome the non-intuitiveness of single acceleration signal and difficulties of feature selection manually, 32 common features are computed and PCA is used for feature reduction. The membership functions are obtained from training data, and fuzzy rules are in same form for all classes. Thus, the FIS is not dependent on expert knowledge of physical activity. Thus, this system is extendable on new types of activity, new features or new locations. This system is designed for a real-time sensorbased monitoring system to recognize 6 types of daily physical activities. Sitting, standing, walking, going upstairs, going downstairs and running are classified with a precision of 99.78%, 90.78%, 91.89%, 89.72%, 91.28% and 100% for each type.