Inferring a user’s high-level goals from low-level sensor readings has been drawing increasing attention from both AI and Pervasive Computing communities recently. A common assumption made by most approaches is that a user has a single goal in mind or aims to achieve several goals sequentially. However, in real-world environments, a user often has multiple goals concurrently carried out and a single action can serve as a step towards multiple goals. In this paper, we formulate the multiple-goal recognition problem and exemplify it in an indoor environment where an RF-based wireless network is available. We propose a recognition algorithm based on a dynamic model set and show how goal models evolve over time among pre-defined states to perform recognition. Experiments with real data demonstrate that our method can accurately and efficiently recognize multiple goals in a user’s trace.