The theme of this master’s thesis is pattern classification for patterns which are vectors of linguistic variables. Firstly, fuzzy set and fuzzy number are defined, and the extension principle for algebraic operations on the set of fuzzy numbers is given. The definition of linguistic variable is introduced, and special types of linguistic variables – Boolean and structured ones are described. Secondly, the n-dimensional space of linguistic variables is defined and (pseudo)metric functions on that space are introduced. The normalization algorithm for linguistic variables is given. Formulas for (pseudo)metric functions are applied in n-dimensional space of trapezoidal linguistic variables. Crisp q-NN (nearest neighbor) and q-NP (nearest prototype) classification algorithms are described and their fuzzy versions are given. Those fuzzy q-NN and q-NP algorithms are adopted for classification of the patterns which are vectors of linguistic variables. Finally, classifier of A. K. Nath and T. T. Lee is compared with fuzzy q-NN and q-NP classification algorithms for patterns that are vectors of linguistic variables. In the Appendix A program documentation for program that classifies n-dimensional vectors of linguistic variables using fuzzy q-NN and q-NP classification algorithms is given, and this program is recorded on CD-ROM and added to the thesis.