Recognizing patterns of human activities is an important enabling technology for building intelligent home environments. Existing approaches to activity recognition often focus on mutually exclusive activities only. In reality, people routinely carry out multiple concurrent activities. It is therefore necessary to model the co-temporal relationships among activities. In this paper, we propose using Factorial Conditional Random Fields (FCRFs) for recognition of multiple concurrent activities. We designed experiments to compare our FCRFs model with Linear Chain Condition Random Fields (LCRFs) in learning and performing inference with the MIT House n data set, which contains annotated data collected from multiple sensors in a real living environment. The experimental results show that FCRFs can effectively improve the F-score in activity recognition for up to 8% in the presence of multiple concurrent activities.