The research reported here anticipates the future of smart buildings by developing algorithms that categorize the movements of individuals based on such characteristics as motion vectors, velocity vectors, head orientation vectors and predetermined positions. The intended applications include detecting intrusions, helping lost visitors, and changing the artwork on virtual posters to reflect an individual's presumed interests. The vectors we capture represent trajectories in a multi-dimensional space. To make sense out of these, we first segment a trajectory into sub-trajectories, typically based on time. To describe each sub-trajectory, we use primitive patterns of body movement and additional information, e.g., average speed during this interval, head movement and place or object nearby. That is, for each sub-trajectory, we use a tuple of the following form: (interval_ID, body_movement, avg_speed, head_movement, places_passed). Since trajectories may have many outliers introduced by sensor failures or uneven human movement, we have developed a neural network-based pattern extraction subsystem that can handle intervals with noisy data. The choice of these attributes and our current classification of behaviors do not imply that these are the only or best ways to categorize behaviors. However, we do not see that as the focus of the research reported here. Rather, our goal is to show that the use of primitive attributes (low level), neural networks to identify categories of recognizable simple behaviors (middle level) and a regular expression-based means of describing intent (high level) is sufficient to provide a means to convert observable low-level attributes into the recognition of potential intents.