Image　representation　has　been　a　key　issue　in　vision　research　for　many　years.　In　order　to　represent　various　local　image　patterns　or　objects　effectively,　it　is　important　to　study　the　spatial　relationship　among　these　objects,　especially　for　the　purpose　of　searching　the　specific　object　among　them.　Psychological　experiments　have　supported　the　hypothesis　that　humans　cognize　the　world　using　visual　context　or　object　spatial　relationship.　How　to　efficiently　learn　and　memorize　such　knowledge　is　a　key　issue　that　should　be　studied.　This　paper　proposes　a　new　type　of　neural　network　for　learning　and　memorizing　object　spatial　relationship　by　means　of　sparse　coding.　A　group　of　comparison　experiments　for　visual　object　searching　between　several　sparse　features　are　carried　out　to　examine　the　proposed　approach.　The　efficiency　of　sparse　coding　of　the　spatial　relationship　is　analyzed　and　discussed.　Theoretical　and　experimental　results　indicate　that　the　newly　developed　neural　network　can　well　learn　and　memorize　object　spatial　relationship　and　simultaneously　the　visual　context　learning　and　memorizing　have　certainly　become　a　grand　challenge　in　simulating　the　human　vision　system.　(C)　2008　Elsevier　B.V.　All　rights　reserved.