A soundscape assessment method that is suitable for the automatic categorization of binaurally recorded sound in urban public places is presented. Soundscape categories are established as a result of an automatic clustering lgorithm based on multi-parameter analysis by 13 acoustical parameters used as similarity measures, on a large set of sound recordings. One of the main advantages of the followed approach allows to take into account an optimized set of parameters that are judged relevant and necessary for an appropriate description of the sampled acoustical scenarios. The Euclidian distance based clustering of the 370 recordings of typical situations based on these parameters, allows to categorize each binaurally recorded sound sample into one of 20 proposed clusters (soundscape categories). The common features among members within each cluster allow to identify ‘‘how the acoustical scenario of the members sounds like’’. The hybrid use of an optimized set of standard acoustical quantities, such as sound pressure level, together with well known psychoacoustical parameters that directly relate to human perception of sound, makes the propose method very robust.