Intelligent environment research has benefited medical care in a number of ways, including emergency detection, comfort and accessibility. However, most of these techniques have been applied in the context of a single resident, leaving out situations where there is more than one person in the living space. A current looming issue for intelligent environment systems is performing these same techniques when multiple residents or care providers are present in the environment. In this paper we investigate the problem of attributing sensor events to individuals in a multi-resident intelligent environment. Specifically, explore and contrast using two different classification techniques. The naive Bayesian and Markov Model classifiers present different capabilities and features for identifying the resident responsible for a unique sensor event. We present results of experimental validation in an intelligent workplace testbed and discuss the unique issues that arise in addressing this challenging problem.