Networked embedded systems are composed of a large number of distributed nodes that interact with the physical world via a set of sensors and actuators, have their own computational capabilities, and communicate with each other via a wired or wireless network. Diagnostic systems for such applications must address new challenges caused by the distribution of resources, the networking environment, and the tight coupling between the computational and the physical worlds. Our approach is to move from centralized, discrete or continuous techniques toward a distributed, hybrid diagnosis architecture. This paper demonstrates distributed, discrete diagnosis algorithms that leverage the topology of the physical plant to limit inter-diagnoser communication and compute diagnoses in an anytime and any information manner, making them robust to communication and processor failures. It also presents a particle filtering based estimation algorithm that addresses the challenge of the interaction between continuous and discrete dynamics in hybrid systems. The distributed qualitative diagnosis and hybrid estimation techniques are demonstrated using a rocket propulsion system.