The　brake　friction　materials　in　an　automotive　brake　system　are　considered　as　one　of　the　key　components　for　overall　braking　performance　of　a　vehicle.　Temperature　sensitivity　of　friction　materials　has　always　been　a　critical　aspect　while　ensuring　their　smooth　and　reliable　functioning,　and　that　sensitivity　need　to　be　constantly　optimized.　The　performance　of　friction　materials　at　elevated　temperatures　is　defined　by　their　fading　performance.　In　this　paper,　a　group　of　non-asbestos　organic　based　friction　materials　containing　different　relative　amounts　of　the　ingredients　of　aluminosilicate　fiber,　steel　fiber,　phenolic　resin　and　Cu　powder　mostly　effecting　on　the　tribological　properties　of　designed　brake　material　were　manufactured　by　compression　molding.　Dry　sliding　friction　characteristics　of　composites　were　tested　on　a　model　JF150D-II　pad-on-disk　type　friction　tester.　A　model　of　feed-forward　artificial　neural　networks　(ANN)　consisting　of　four　input　neurons,　six　output　neurons　and　one　hidden　layer,　was　used　for　the　analysis　and　prediction　of　the　correlation　between　material　components　and　friction　performance.　The　input　parameters　of　ANN　were　the　contents　of　four　main　components　as　aluminosilicate　fiber,　steel　fiber,　phenolic　resin　and　Cu　powder.　The　outputs　were　the　friction　coefficients　of　brake　material　against　cast　iron　at　six　different　operating　tempetures　from　100°C　to　350°C.　Based　on　ANN　model　trained　successfully　by　25　pieces　samples,　genetic　algorithm(GA)　was　used　to　optimize　the　input　parameters　of　compositions　of　brake　material　with　the　goal　of　minimizing　fluctuation　of　friction　coefficients　at　100°C　to　350°C.　The　optimum　composition　of　brake　material　was　obtained.　The　friction　experiments　of　optimized　material　showed　excellent　stability　of　friction　coefficients　for　automotive　brake　material　as　test　temperatures　increased　from　100°C　to　350°C.　The　neural　network　has　impressive　potential　for　solving　time-consuming　problems　for　the　design　of　automobile　brake　material.