Considering this fundamental problem in MFSC scoring system, we suggest that assessing MS progression by using a fuzzy-inference system can be a more reasonable and practically applicable option than the simple average of the three areas of assessment currently employed.5 The fuzzy-inference system is similar to human reasoning and is basically developed to model complex and nonlinear systems as a set of rules with a simple conceptual structure. In fuzzy logic, which is the basis of fuzzy-inference systems, the intrinsic value to the fuzzy sets is an indication of the status of each item relative to its normal value. Each value resulting from the three areas of assessment has its own fuzzy set, and its parameters are obtained from the existing statistical norms. Thus, each broad area of assessment (Lower Extremity, Upper Extremity, and Cognition) will have three intrinsic values related to the three corresponding fuzzy sets. These values and their combinations will lead to better scores, reflecting MS progression better than the current MSFC, which only considers the average. In a fuzzy-inference system, the fuzzy output value is calculated through the T-norm operation, which can be of maximum value, for product, average, and so forth, and this gives the capability of assigning different weights to each input value. What follows is the degree of MS progression according to the three weighted criteria, converted to the three separate intrinsic values. A simple average value cannot give a specific assessment of MS progression. Also, the same two conventionally-obtained output values mean the same results; however, in the fuzzy concept, the output fuzzy value is specifically related to the output fuzzy set, which is also deterministically constructed by the application of a T-norm operation on inputted fuzzy sets and group values.