We consider the crisp and fuzzy partitioning techniques of cluster analysis bearing in mind their application for classification of data obtained with chemical sensor arrays. The advantage of the cluster analysis techniques is existence of a parameter S(i). This parameter gives quantitative efficiency of classification and can be used as optimization criterion for sensor system as a whole as well as the measurement procedure. The crisp and fuzzy techniques give practically the same result when analyzing the data that cluster uniquely. It is shown that big value of the parameter S(i) is not sufficient for adequate data partitioning into cluster in more complicated cases, and the results of clusterization for the above techniques may diverge. In this case, one should apply both techniques concurrently, checking the correctness of partitioning into clusters against the principal component analysis.