The feasibility of sensor fault detection using noise analysis is evaluated. The noise powers at various frequency bands present in the sensor output are calculated using power spectrum density estimation and compared with historically established noise pattern to identify any abnormalities. The method is applicable to systems for which the noise is stationary under normal operating conditions. Principal component analysis (PCA) is used to reduce the space of secondary variables derived from the power spectrum. T-2 statistics is used to detect deviations from the norm. We take advantage of the low-pass filtering characteristics exhibited by most process plants and closed-loop control systems, which allows the noise power at higher frequency bands to be used in the fault detection. The algorithm does not require a process model because it focuses on characterization of each individual sensor and the measurement it generates. Experimental studies with two kinds of garden variety sensors (off the shelf temperature and pressure sensors) are used to validate the feasibility of the proposed approach.