In this paper an algorithm for data-driven incrementallearning of fuzzy basis function networks is presented. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The premisepart learning is connected in a stable manner with a recursive learning of rule consequent functions possessing linear parameters. The paper is concluded with an evaluation of the proposed algorithm on high-dimensional measurement data for engine test benches.