Recently,　a　neural　dynamical　approach　to　solving　linearly　constrained　variational　inequality　problems　is　presented,　and　its　stability　and　convergence　are　conjectured　by　simulation.　This　technical　note　analyzes　the　global　stability　and　convergence　of　the　neural　dynamical　approach.　Theoretically,　it　is　shown　that　the　neural　dynamical　approach　is　convergent　globally　to　a　solution　when　the　nonlinear　mapping　is　monotone　at　the　solution.　Unlike　existing　convergence　results　of　neural　dynamical　methods　for　solving　linearly　or　nonlinearly　variational　inequalities,　our　main　results　don＇t　assume　the　differentiability　condition　of　the　nonlinear　mapping.　Therefore,　the　neural　dynamical　approach　can　be　further　guaranteed　to　solve　linearly　constrained　monotone　variational　inequality　problems　with　a　non-smooth　mapping.　Comparsions　and　examples　illustrative　significance　of　the　obtained　results　on　non-smooth　mapping.　©　2009　IEEE.