Plan recognition has traditionally been developed forlogically encoded application domains with a focus onlogical reasoning. In this paper, we present an integrated plan-recognition model that combines low-levelsensory readings with high-level goal inference. A twolevel architecture is proposed to infer a user’s goals ina complex indoor environment using an RF-based wireless network. The novelty of our work derives from ourability to infer a user’s goals from sequences of signal trajectory, and the ability for us to make a tradeoff between model accuracy and inference efficiency.The model relies on a dynamic Bayesian network to infer a user’s actions from raw signals, and an N-grammodel to infer the users’ goals from actions. We presenta method for constructing the model from the past dataand demonstrate the effectiveness of our proposed solution through empirical studies using some real data thatwe have collected.