This dissertation tackles the problem of statistical control of multi-channel processes, which has been scarcely dealt with in the specialized literature. The techniques found in the literature assume validity conditions which are not always verified in practice, namely: successive measurements taken in each channel should be independent an identically distributed; all the channels should be adjusted, with same mean and standard deviation; and most works assume the channels to be independent, with no cross correlation. The study was motivated by a real case, in which no one of these conditions holds. This work proposes adaptations and extensions of existing techniques in order to deal with this and similar real situations; details the application of the adapted/extended techniques; illustrates their application in the case under analysis; discusses limitations and proposes alternatives, and initiates a discussion about the different conditions (practical situations´ characteristics) in which each alternative is most appropriate. The work began with an exploratory data analysis of the enterprise´s production processes, so as to enable a diagnosis of the statistical process control procedures that were in use, and serve as a basis for the proposal of a new, more adequate, control scheme. The proposed scheme, when applied to the data, signalled problems with the processes which the techniques in use did not signal. Due to the deadlines for ending this dissertation and to programmed interruptions in the production, it has not been possible to include in the analysis the search for special causes with corresponding revisions of the charts´ control limits and the use of the revised charts in on-line monitoring, for the purposes of feedback on the proposed scheme´s performance. Its application to the available data has nevertheless shown it more sensitive to special causes than the charts that were in use, and raised some issues not approached in the literature, which are left as indications for future research.