The sensor validation or SEVA project (Henry and Clarke 1991; Henry and Clarke 1993) promotes the use of intelligence in ‘smart’ sensors and the use of standard metrics to efficiently communicate self-diagnostics to the outside world. The standard metrics describe the status of the sensor including on-line uncertainty and a status flag to describe how the current validated measurement value has been derived. The end result is to provide a compact generic description of the quality of a measurement to the controller, with which decisions as to how to use the measurement can be made. This paper proposes the use of SEVA principles in the interpretation of data from biomedical instrumentation, in order to aid the decision-making process, particularly in critical care. For these purposes the pulse oximeter and polarographic oxygen tension meter will be used as working examples of typical ‘intelligent sensors’ because they make use of a microprocessor to perform self-diagnostics, as well as implementing measurement algorithms.