Most　of　vision　based　transport　parameter　detection　algorithms　are　designed　to　be　executed　in　good-natured　weather　conditions.　However,　limited　visibility　in　rain　or　fog　strongly　influences　detection　results.　To　improve　machine　vision　in　adverse　weather　situations,　a　reliable　weather　conditions　detection　system　is　necessary　as　a　ground　base.　In　this　article,　a　novel　algorithm　for　weather　condition　automatic　recognition　is　presented.　This　proposed　system　is　able　to　distinguish　between　multiple　weather　situations　based　on　the　classification　of　single　monocular　color　images　without　any　additional　assumptions　or　prior　knowledge.　Homogenous　area　is　extracted　form　top　to　bottom　in　scene　image.　Inflection　point　information　which　implies　visibility　distance　will　be　taken　as　a　character　feature　for　current　weather　recognition.　Another　four　features:　power　spectral　slope,　edge　gradient　energy,　contrast,　saturation,　and　image　noisy　which　descript　image　definition　are　extracted　also.　Our　proposed　image　descriptor　clearly　outperforms　existing　descriptors　for　the　task.　Experimental　results　on　real　traffic　images　are　characterized　by　high　accuracy,　efficiency,　and　versatility　with　respect　to　driver　assistance　systems.　©　Springer-Verlag　Berlin　Heidelberg　2014.