Constrained　optimization　problems　arise　in　a　wide　variety　of　scientific　and　engineering　applications.　Since　several　single　recurrent　neural　networks　when　applied　to　solve　constrained　optimization　problems　for　real-time　engineering　applications　have　shown　some　limitations,　cooperative　recurrent　neural　network　approaches　have　been　developed　to　overcome　drawbacks　of　these　single　recurrent　neural　networks.　This　paper　surveys　in　details　work　on　cooperative　recurrent　neural　networks　for　solving　constrained　optimization　problems　and　their　engineering　applications,　and　points　out　their　standing　models　from　viewpoint　of　both　convergence　to　the　optimal　solution　and　model　complexity.　We　provide　examples　and　comparisons　to　shown　advantages　of　these　models　in　the　given　applications.　©　2008　Springer　Science+Business　Media　B.V.