On line monitoring and diagnosis systems of process operating performance are becoming important part of industrial programs, leading to improve process operation and therefore product quality over time. In processes, such as flotation, a large number of input variables, highly correlated, are presented. These characteristics usually pose more difficulties in modelling the process for monitoring and diagnosis purposes. Multivariate statistical projection methods have been proposed to effectively deal with these situations. The application of these ideas to a column flotation process is discussed here. In this work, the detection of measurement problems in relevant variables, and the identification of the set of variables responsible for driving the process outside its normal operating region, are demonstrated. Furthermore, the use of these techniques gave considerable information for the correction of the operating problem. © 2005 Elsevier Ltd. All rights reserved.