Distributed sensor networks are highly prone to accidental errors and malicious activities, owing to their limited resources and tight interaction with the environment. Yet only a few studies have analyzed and coped with the effects of corrupted sensor data. This paper contributes with the proposal of an on-the-fly statistical technique that can detect and distinguish faulty data from malicious data in a distributed sensor network. Detecting faults and attacks is essential to ensure the correct semantic of the network, while distinguishing faults from attacks is necessary to initiate a correct recovery action. The approach uses Hidden Markov Models (HMMs) to capture the error/attack-free dynamics of the environment and the dynamics of error/attack data. It then performs a structural analysis of these HMMs to determine the type of error/ attack affecting sensor observations. The methodology is demonstrated with real data traces collected over one month of observation from motes deployed on the Great Duck Island.