Failures in a plant's electrical components are a major source of performance degradation and plant unavailability, In order to detect and monitor failure precursors and anomalies early in electrical systems, we have developed signal processing capabilities that can detect and map patterns in already existing and available signals to an anomaly measure. Toward this end, the language measure theory based on real analysis, finite state automaton, symbolic dynamics and information theory has been deployed. Application of this theory for electronic circuit failure precursor detection resulted in a robust statistical pattern recognition technique. This technique was observed to be superior to conventional pattern recognition techniques such as neural networks and principal component analysis for anomaly detection because it exploits a common physical fact underling most anomalies which conventional techniques do not. Symbolic dynamic technique resulted in a monotonically increasing smooth anomaly plot which was experimentally repeatable to a remarkable accuracy. For the Van der Pol oscillator circuit board experiment, this lead to consistently accurate predictions for the anomaly parameter and its range.