This paper describes a data generator to synthesize sensor observations in the context of environment monitoring. The overall goal of our work is to monitor the well-being of occupant(s) in a home. Sensors are embedded in a smart home to unobtrusively record environmental parameters. Based on the sensor observations, behavior analysis and modeling are performed. Behavior modeling and analysis require large data sets to be collected over long periods of time to achieve the level of accuracy expected. A data generator – was developed based on initial data i.e. data collected over periods lasting weeks to facilitate concurrent data collection, and development of modeling algorithms. The data generator is based on statistical inference techniques to produce models. Variation is introduced into the data by perturbing the models.