When a complex plant is operating, the output of the plant sensors form a pattern of readings that represent the state of the plant. When disturbances occur, the sensor outputs form patterns that represent different states of the plant, depending on the nature of the disturbances. For safety reasons, it is important to identify changes in the state of operation at the earliest possible time. In this study, we have applied a neural network technique to data from a commercial nuclear power plant to detect changes in operational states. The Polynomial Discriminant Method, PDM, was used to identify operational states of a pressurized water reactor nuclear plant. The data consisted of temperature, pressure, power level, and coolant flow readings collected in one of the loops of the plant. The network was trained to discriminate among data collected during normal, steady-state, operating conditions; data collected during start-up; and transient data collected during a shut-down. The network was able to classify correctly 89 % of the cases in which it was tested. The algorithm, although intensively computational during training, exhibits rapid matching and great interpolating capabilities during recall.