According　to　the　field　data　of　surface　water　quality　from　Beijing　Water　Authority,　this　paper　demonstrates　a　case　study　on　how　to　utilize　principle　factor　analysis　and　hierarchical　cluster　analysis　to　extract　a　limited　number　of　principal　factors　that　can　best　describe　the　original　data　and　to　identify　the　patterns　of　surface　water　quality　pollution.　10　auto-monitoring　sites　dispense　in　Beijing　are　selected　as　study　objects,　and　according　to　actual　monitoring　data　of　comprehensive　database　from　Beijing　Water　Authority,　9　indicators　including:　WT,　PH,　NO3N,　NH4N,　DO,　CNDR,　TRB,　DLS　and　CHLA　are　selected　for　principle　factor　analysis　and　hierarchical　cluster　analysis.　Based　on　the　monitoring　data　during　2010,　principle　factor　analysis　is　utilized　to　reflect　those　chemical　data　with　the　greatest　correlation,　and　the　results　identify　four　principal　factors　representing　92.432%　of　cumulative　variance　(or　total　information).　By　utilizing　principle　factor　analysis,　thermal　pollution　factor,　nitrate　pollution　factor,　plankton　pollution　factor　and　ammonia　nitrogen　pollution　factor　reasonably　interpret　the　main　factors　of　surface　water　quality　pollution.　Based　on　factors＇　scores,　the　level　of　the　comprehensive　water　quality　pollution　about　the　10　current　auto-monitoring　sites　is　obtained,　by　which　condition　of　surface　water　quality　pollution　is　sorted.　The　results　show　that　Gaobeidian　and　Sanjiadian　are　heavily　polluted,　which　are　tally　with　Beijing　Water　Resources　Bulletin　of　2010.　Further　more,　in　support　of　the　results　obtained　by　principle　factor　analysis,　5　clusters　are　assigned　by　HCA　to　evaluate　the　similarities　of　water　quality　among　the　10　automonitoring　sites.　In　allusion　to　the　different　patterns　of　surface　water　quality　pollution　and　the　influencing　factors,　suitable　microscopic　measures　can　be　proposed　for　different　areas,　which　can　provide　a　base　for　macroscopic　planning　of　the　city.　©　2011　IEEE.