We present Propagation Networks (P-Nets), a novel approach for representing and recognizing sequential activities that include parallel streams of action. We represent each activity using partially ordered intervals. Each interval is restricted by both temporal and logical constraints, including information about its duration and its temporal relationship with other intervals. P-Nets associate one node with each temporal interval. Each node is triggered according to a probability density function that depends on the state of its parent nodes. Each node also has an associated observation function that characterizes supporting perceptual evidence. To facilitate realtime analysis, we introduce a particle filter framework to explore the conditional state space. We modify the original Condensation algorithm to more efficiently sample a discrete state space (D-Condensation). Experiments in the domain of blood glucose monitor calibration demonstrate both the representational power of P-Nets and the effectiveness of the D-Condensation algorithm.