As the world population is growing older, more and more peoples are facing health issues. For elderly, leaving alone can be tough and risky, typically, a fall can have serious consequences for them. Consequently, smart homes are becoming more and more popular. Such sensors enriched environment can be exploited for health-care applications, in particular Anomaly Detection (AD). Currently, most AD solutions only focus on detecting anomalies in the user daily activities while omitting the ones from the environment itself. In this paper, we present a novel approach for detecting anomaly occurring in the home environment during user activities.. We propose an application of the Markov Logic Network to classify the situations to anomaly classes. Our system is implemented, tested and evaluated using real data obtained from the Hadaptic platform. Experimental results prove our approach to be efficient in terms of recognition rate.