A novel, automated method based on principal component analysis is presented for the detection and identification of disturbed sensors during a process monitoring application. As opposed to previous approaches, which are capable of identifying a fault in only a single sensor, the backward elimination sensor identification (BESI) algorithm is presented, which can identify upsets in multiple sensors. In the method, disturbed sensors are identified sequentially, or one at a time, using a residual-based criterion. The BESI algorithm is sensitive to changes in sensor correlations detected by conventional multivariate statistical process control but offers the ease of interpretation of conventional univariate methods. The BESI algorithm is successfully employed in the identification of disturbed sensors in simulated spectroscopic data and industrial process data. By examining the disturbance profiles generated over time by the BESI algorithm, it is also possible to distinguish between sensor and process disturbances.