A fuzzy clustering approach is studied for optimal pole selection of Orthonormal Basis Functions (OBFs) used for the identification of Linear Parameter Varying (LPV) systems. The identification approach is based on interpolation of locally identified Linear Time Invariant (LTI) models, using globally fixed OBFs. The selection of the optimal OBF structure, that guarantees the least worst-case local modelling error, is accomplished through the joint application of the Kolmogorov n-width theory and Fuzzy c-Means (FCM) clustering of observed sample system poles. For the problem at hand, FCM solutions are given, based on three different metrics, and the qualities of the results are compared in terms of the derived OBFs.