Statistical control is important because it shows you what the process fs capable of producing over time. It is useful to make predictions about how the process will be in the future, based in how it was in the past. The studies about quality are continuous due to the exigent demands of the market, to the competency and to the rapid advance of technology and science. That is the reason of looking for new tools or methods that complement, facilitate and optimize the use of the actual tools. It is important to understand that the main goal of the statistical process control is the elimination of the variability of the process. It is not possible to eliminate variability to the zero, but the control charts are useful tools, which make reduction it to its minimum possible. Control charts are one the most technically sophisticated tools of Statistical Quality Control (SQC). They are used for analysis of common and special causes of process variations. There are different types of control charts according to the type of characteristic to be measured. First there are control charts for attributes, which are used to measure qualitative variables and cannot be expressed numerically; and second there are also control charts for variables, which are specially and extensible used for manufacturing process [2]. These charts are used to evaluate quantitative variables in the process that means, all characteristics of quality that can be expressed numerically. The goal of this paper is to show the results of experiments made with new types of control charts. This new class of chart diagrams we have named "class of normalized chart diagrams". The new types of control charts applied were: The new types of charts might be useful in many cases for investigation of stability and quality of a manufacturing process. The data used for the experiments was partially simulated using the QC Expert software and partially have been used the data from factory Polovodice, a. s. (Semiconductors, Ltd).
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