Using　guided　circumferential　wave　dispersion　characteristics,　an　inverse　method　based　on　artificial　neural　network　(ANN)　is　presented　to　determine　the　material　properties　of　functionally　graded　material　(FGM)　pipes.　The　group　velocities　of　lowest　modes　at　six　lower　frequencies　are　used　as　the　inputs　of　the　ANN　model.　The　distribution　function　of　the　volume　fraction　of　the　FGM　pipe　is　fitted　to　a　polynomial,　then　the　outputs　of　the　ANN　are　the　coefficients　of　the　fitting　polynomial.　The　Legendre　polynomial　method　is　employed　as　the　forward　solver　to　calculate　the　dispersion　curves　for　the　FGM　pipe.　Levenberg-Marquardt　algorithm　is　used　as　numerical　optimization　to　speed　up　the　training　process　of　the　ANN　model.　(C)　2009　Elsevier　Ltd.　All　rights　reserved.