This　paper　presents　a　clustering　algorithm　based　on　maximal　theta-distant　subtrees,　the　basic　idea　of　which　is　to　find　a　set　of　maximal　theta-distant　subtrees　by　threshold　cutting　from　a　minimal　spanning　tree　and　merge　each　of　their　vertex　sets　into　a　cluster,　coupled　with　a　post-processing　step　for　merging　small　clusters.　The　proposed　algorithm　can　detect　any　number　of　well-separated　clusters　with　any　shapes　and　indicate　the　inherent　hierarchical　nature　of　the　Clusters　present　in　a　data　set.　Moreover,　it　is　able　to　detect　elements　of　small　clusters　as　outliers　in　a　data　set　and　group　them　into　a　new　cluster　if　the　number　of　outliers　is　relatively　large.　Some　computer　simulations　demonstrate　the　effectiveness　of　the　clustering　scheme.　(c)　2006　Pattern　Recognition　Society.　Published　by　Elsevier　Ltd.　All　rights　reserved.