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Abstract: Carbon monoxide (CO) dramatically reduces the performance of a fuel cell stack if not remediated. Remediation generally requires parasitic bleeding of a small fraction (5%) of air into the fuel stream to promote oxidation of the CO and use of a platinum-ruthenium or other noble metal based catalyst. For enhancement of system efficiency, air bleed should be controlled using real-time feedback of CO level in the feed-stream. In this paper, a recently reported data-driven pattern identification method, called Symbolic Dynamic Filtering (SDF), is applied for on-line sensing of CO content in an impure reformed hydrogen fuel stream. A small fuel cell, fuelled by a diverted stream of reformate, is used as a CO sensor. CO level is determined through time series analysis of the dynamic current response of the sensor cell due to load oscillations. The pattern identification algorithms are built upon the underlying concepts of Symbolic Dynamics, Information Theory and Probabilistic Finite State Machines. The effect of temperature on sensitivity was analyzed, and results demonstrate the efficacy of CO sensor under different operating conditions. The sensitivity of the CO sensor can be tailored for a particular application by changing the type of catalyst, its loading and operation temperature. A similar approach is now being used to develop online sensors for a variety of other important fuel cell phenomena, such as flooding and catalyst degradation. [Copyright &y& Elsevier]