In order to resolve the computational complexity for local map matching of hierarchical simultaneous localization and mapping (SLAM), a novel self-organizing fuzzy neural networks (SOFNN) based approach was proposed in this paper. The matching component for local maps in the hierarchical SLAM is realized by an SOFNN. A subset of signature elements included in a local map was chosen by a clustering algorithm, then was inputted to the SOFNN. The criteria to complete a local map, and the structure learning and parameter learning algorithms for our SOFNN were discussed.