Pattern recognition methods and hidden Markov models can be effective tools for online health monitoring of communications systems. Previous work has assumed that the states in the system model are exhaustive. This can be a significant drawback in real-world fault monitoring applications where it is difficult if not impossible to model all the possible fault states of the system in advance. In this paper a method is described for extending the Markov monitoring approach to allow for unknown or novel states which cannot be accounted for when the model is being designed. The method is described and evaluated on data from one of the Jet Propulsion Laboratory's Deep Space Network antennas. The experimental results indicate that the method is both practical and effective, allowing both discrimination between known states and detection of previously unknown fault conditions.< >