We present a new methodology for use in automated monitoring of a system to detect abnormal or degraded system behavior. The methodology uses the singular value decomposition technique of matrix algebra to discover relationships between the elements of data observed from a normally operating system. It then tests these relationships against newly acquired data to detect system malfunctions. Based on a theory for system identification in the presence of nuisance parameters, distribution theory developed specifically for this application allows the system monitor to properly set threshold levels so as to control the false alarm rate. The algorithm is numerically stable and relatively easy to apply, and was tested successfully on data from a motorized globe valve in a nuclear power plant.