A　continuous　recurrent　neural　network　model　is　presented　for　computing　the　largest　and　smallest　generalized　eigenvalue　of　a　symmetric　positive　pair　(A,B).　Convergence　properties　to　the　extremum　eigenvalues　based　upon　Liapunov　functional　with　the　help　of　the　generalized　eigen-decomposition　theorem　is　obtained.　Compared　with　other　existing　models,　this　model　is　also　suitable　for　computing　the　smallest　generalized　eigenvalue　simply　by　replacing　A　by　-A　as　well　as　maintaining　invariant　norm　property.　Numerical　simulation　further　shows　the　effectiveness　of　the　proposed　model.　(C)　2008　Elsevier　B.V.　All　rights　reserved.