The solution of most MCDM-problems involves measuring the characteristics of a research object, converting the estimations into a confidence distribution specified on a set of qualitative gradations and aggregating the estimations in accordance with the structure of the criteria system. The quality of the problems solution as a whole directly depends on the quality of measuring the characteristics of a research object. Data for obtaining estimations of the characteristics are often inaccurate, incomplete, approximate. Modern researches either fragmentarily touch on the questions of measurement quality, or focus on other questions. Our goal is to choose such parameters for converting the value of the quantitative characteristic of a research object into a confidence distribution, which provide the best measurement quality. Based on the observation channel (OC) concept proposed by G. Klir, we refined the measurement quality criteria, determined the composition of the OC parameters, developed an algorithm for calculating the measurement quality criteria and choosing the best OC for the most common MCDM-problems. As calculations have shown, in the most common MCDM-problems, the best is OC, which is built on the basis of a bell-shaped membership function and has a scale of seven blocks. The obtained result will allow researchers to justify the choice of OC parameters from the viewpoint of the maximum quality of measuring the quantitative characteristics of a research object in MCDM-problems and uncertainty conditions.