Manhattan world are referred to manmade structures with planar surfaces in a scene. In many applications such as robot navigation or mapping is vital to have a 3d perception of such environments. In this thesis a novel methodology is presented to perceive slant surfaces implementing 3D point clouds. Using an enhanced Ncut clustering technique, the point cloud is classified into a number of clusters. Normally in a Manhattan scene one or more of such clusters have a planar nature. To automatically perceive the existents of such plane a series of algorithms is implemented which are consist of ; LS fitting, pruning and RANSAC. Experiments were carried out in MATLAB for both simulated data and real world data. 5 scenes were simulated with 10 noise level in a way that they would resemble the real world data. In addition, Prime Scene sensor was used to collect data from 5 different scenes. In total, the algorithm was tested on 100 point clouds. According to the obtained results, the proposed methodolgy was able to successfully extract planar surfaces in each scene. The performance of the enhanced Ncut algorithm is compared with previous methods of K-means and the original Ncut where each of them separately used in the methodology. The results confirmed significant improvement of the enhanced Ncut over the original Ncut and K-means method. Also our finding has already been published as a conference paper and it has been submitted for a journal publication. These can be seen as good indicators for novelty of the methodology.