In　this　paper,　Bayesian　Regularized　Back　Propagation　Neural　Network　(BRBPNN)　technique　was　applied　in　seismic　damage　prediction　on　buildings.　The　seismic　damage　influences　factors　are　summarized　as　nine　input　parameters　for　the　network.　They　are　site　condition　X-1,　integrity　of　building　X-2,　building　shape　X-3,　building　height　X-4,　mortar　grade　of　wall　at　bottom　X-5,　area　ratio　of　brick　wall　X-6,　X-7,　building　format　X-8　and　seismic　intensity　X-9.　From　earthquakes　of　Langeang-gengma　(1988),　Wuding　(1995),　Lijiang　(1996)　and　Tanshang　(1978),　50　seismic　damage　samples　for　multistory　masonry　buildings　were　collected.　After　checking　the　square　weights　of　the　input　layers　of　the　BRBPNN　models,　the　factor　X-3　is　found　to　be　not　efficient.　By　contrasting　the　Mean　Square　Errors　(MSE)　on　the　training　and　testing　samples　of　different　models,　the　model　8-5-5　is　recommended.　Increase　of　the　neurons　in　the　hidden　layer　has　little　influence　to　the　efficiency　of　the　neural　network.　The　importance　of　the　factors　is　listed　as　X-9,　X-7,　X-6,　X-4,　X-8,　X-2,　X-1,　and　X-5.　The　scatter　diagrams　show　that　the　effect　of　each　factor　on　the　seismic　damage　of　multistory　masonry　buildings.　The　factor　like　X-9,　X-1,　X-2,　and　X-8　are　more　dependent　for　the　problem,　although　they　all　seem　to　be　nonlinear　with　the　seismic　damage.　Results　show　that　Bayesian　Regularized　Back　Propagation　Neural　Network　is　of　automated　regularization　parameter　selection　capability　and　thus　may　ensure　the　excellent　generation　ability　and　robustness.　This　study　threw　some　light　on　the　applicability　of　seismic　damage　prediction　on　the　multistory　masonry　buildings.