As　a　core　part　of　the　electronic　warfare　(EW)　system,　de-interleaving　is　used　to　separate　interleaved　radar　signals.　As　interleaved　radar　pulses　become　more　complex　and　denser,　intel　i-gent　classification　of　radar　signals　has　become　very　important.　The　self-organizing　feature　map　(SOFM)　is　an　excel　ent　artificial　neural　network,　which　has　huge　advantages　in　intel　igent　classification　of　complex　data.　However,　the　de-interleaving　process　based　on　SOFM　is　faced　with　the　problems　that　the　initialization　of　the　map　size　relies　on　prior　information　and　the　network　topology　cannot　be　adaptively　adjusted.　In　this　paper,　an　SOFM　with　self-adaptive　network　topology　(SANT-SOFM)　algorithm　is　proposed　to　solve　the　above　problems.　The　SANT-SOFM　algorithm　first　proposes　an　adaptive　proliferation　algorithm　to　adjust　the　map　size,　so　that　the　initialization　of　the　map　size　is　no　longer　dependent　on　prior　information　but　is　gradual　y　adjusted　with　the　input　data.　Then,　structural　optimization　algorithms　are　proposed　to　gradual　y　opti-mize　the　topology　of　the　SOFM　network　in　the　iterative　process,　constructing　an　optimal　SANT.　Final　y,　the　optimized　SOFM　net-work　is　used　for　de-interleaving　radar　signals.　Simulation　results　show　that　SANT-SOFM　could　get　excel　ent　performance　in　com-plex　EW　environments　and　the　probability　of　getting　the　optimal　map　size　is　over　95％in　the　absence　of　priori　information.