One-dimension　range　profile　has　recently　been　widely　used　as　recognition　feature　in　radar　target　recognition　since　it　can　be　extracted　easily.　However,　the　one-dimension　range　profile　of　a　target　has　strong　dependence　on　the　aspect-angle　of　the　target　because　it　is　based　on　the　model　of　scattering　centers.　On　the　other　hand,　the　different　classes　of　target　samples　overlap　each　other　in　the　feature　space,　so　satisfactory　recognition　results　cannot　be　obtained　directly　for　the　targets　under　whole　posture.　In　order　to　improve　the　recognition　performance　of　systems　based　on　one-dimension　range　profile,　a　method　of　radar　target　recognition　based　on　the　multi-resolution　analysis　theory　and　neural　network　is　presented.　Firstly,　one-dimension　range　profile　features　under　various　resolutions　are　obtained　by　using　multi-resolution　analysis.　Then,　the　tree-structured　cascade　SOM　network　-　a　hybrid　neural　network　on　the　basis　of　the　multi-resolution　analysis　theory　and　self-organizing　map　(SOM)　-　has　been　used　as　the　classifier　to　make　multi-grade　judgment　and　recognition　gradually　for　the　one-dimension　range　profile　features.　The　experimental　results　show　that　the　average　correct　recognition　rate　for　three　kinds　of　targets　-　a　propeller　aircraft,　a　little　jet　aircraft　and　a　large　jet　aircraft　-　with　0-180　degrees　Posture　variation　in　aspect-angle　is　up　to　92.97%.　(C)　2008　Published　by　Elsevier　B.V.