Due　to　the　limitation　of　the　present　techniques　and　facilities　for　data　collection　and　various　interferences,　the　data　obtained　are　often　distorted　and　noised,　directly　influencing　the　result　of　subsequent　data　analysis.　The　conventional　approaches　to　outlier　removal　either　assume　that　the　data　follow　a　certain　known　distribution　or　deal　with　the　data　that　are　from　a　single　distribution,　resulting　in　a　reduced　credibility　of　the　data　processed.　This　paper　proposes　a　novel　method　to　remove　outliers　based　on　density　estimation　and　it　has　been　applied　to　real-world　traffic　data.　By　comparison　with　the　conventional　approach,　the　experimental　results　indicate　that　the　proposed　algorithm　is　capable　of　detecting　and　removing　outliers　effectively　for　the　data　that　may　follow　different　unknown　distributions,　and　the　processed　data　retain　the　original　and　significant　characteristics　possessed　by　the　system.　Copyright　©　2010　Acta　Automatica　Sinica.　All　rights　reserved.