1. A method of designing/configuring a multivariale statistical process monitor by a partial least squares approach comprises constructing from reference data of the process predictor and response matrices, the predictor matrix being comprised of signals of the manipulated and measured disturbance or cause variables of the process (predictor variables), and the response matrix being comprised of the controlled or effect variables of the process (response variables), decomposing the predictor and response matrices into rank one component matrices, each of said component matrices being comprised of a vector product in which one vector (the score vector) describes the variation and the other (the loading vector) the contribution of the score vector to the predictor or response matrix, decomposition being performed by the creation of a parametric regression matrix based upon iterations of the decomposition of the predictor and response matrices, characterised by the creation of a first generalised score vector which describes any significant variation of the process including variations of the predictor and response variables, and a second generalised score vector which represents the prediction error of the partial least squares model and residuals of the predictor matrix.