1. A method of controlling an m variable multivariate system or process, characterized by: (a) obtaining ( 220 ) historical reference data comprising numerical measurements of said m variables, said measurements collectively encompassing a plurality of variations within one or more operational states of said system or process; (b) modeling ( 222 ) said historical reference data to produce modeled values, modeling uncertainties and measuring uncertainties of said reference data set measurements; (c) deriving ( 224 - 230 ) a final model of said historical reference data by sequentially repeating said modeling until successively derived sums of all of said measurement uncertainties are approximately equal and successively derived sums of all of said modeling uncertainties are approximately equal; (d) deriving ( 232 ) a set of modeling parameters characteristic of said final model; and, (e) combining said measuring uncertainties with a new data set ( 200 ) comprising numerical measurements of said m variables, said new data set measurements collectively encompassing said plurality of variations within said one or more operational states of said system or process to control ( 218 ) continued operation of said multivariate system or process.