| Περίληψη : | Statistical quality control is a major branch of science of statistics purporting to detect errors occurring in automated production processes. Statistical quality control is under a continuous, critical evaluation of analytical methods and in-cludes a detailed course / process starting from sample input. The most important tool in quality control is the use of control charts (control charts). This paper attempts a detailed overview of the important elements of Statistical Quality Control. The Statistical Quality Control (Statistical Process Control, SPC) is one of the most reliable tools for the monitoring of the product, ensuring that the processes are under control and also enables business to their long-term survival and especially their profitability. Also, Multivariable Statistical Quality Control (Multivariate Statistical Process Control, MSPC) enables monitoring of two or more variables of a product simultaneously. The need to use multivariate models emerged from the finding that the quality of a product can be associated with more than one quality and measurable characteristics. Of the various tech-niques resulting MSPC charts, whose analysis and monitoring lead to findings whose presence would otherwise be very difficult or impossible. The present study deals with the presentation through the literature of multivari-ate charts as an extension of one-dimensional, which seems to be more prevalent today, and continuing with the description of the profile monitoring. The Profile monitoring is a relatively new technique in statistical quality control is best used when data processing follows a profile (or curve) in each period.
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