In　this　paper,　we　establish　a　neural-network-based　adaptive　dynamic　programming　(ADP)　method　to　address　the　decentralized　control　problem　for　nonlinear　continuous-time　(CT)　interconnected　systems　with　external　disturbances.　First,　the　optimal　control　schemes　of　the　isolated　subsystems　are　constructed,　which　are　based　on　the　appropriate　cost　functions.　Besides,　it　is　accessible　to　establish　the　effective　decentralized　control　strategy　of　the　large-scale　systems　by　adding　suitable　feedback　gains.　Then,　by　taking　into　account　the　disturbance　rejection　issue,　the　Hamilton-Jacobi-Isaacs　(HJI)　equation　can　be　derived.　To　solve　the　HJI　equation,　a　series　of　critic　neural　networks　are　developed　to　obtain　the　cost　functions　and　the　optimal　control　strategies　approximately.　Additionally,　a　novel　weight　updating　rule　is　proposed　during　the　neural　critic　learning　process.　Finally,　the　simulation　results　are　presented　to　display　the　decentralized　control　performance　by　adopting　the　neural-network-based　ADP　method.　©　2022　IEEE.