The use of management zones is considered a viable economic alternative for the management of crops due to low cost of adoption as well as economic and environmental benefits. The decision whether or not to normalize the attributes before the grouping process (independent of use) is a problem of methodology, because the attributes have different metric size units, and may influence the result of the clustering process. Thus, the aim of this study was to use a Fuzzy C-Means algorithm to evaluate the performance of various data normalization techniques used in the data clustering process for generating MZs. The tests were conducted in three experimental areas. A Fuzzy C-Means clustering method using Euclidean distance was used to define MZs in sub-regions two, three, and four of each field, respectively. The conclusion was that the use of normalization techniques is essential for defining MZs with a Fuzzy C-Means algorithm using the Euclidean distance when there is a need to use more than one attribute with different metric units.