Automatic detection of the pavement damages due to various actions of frost and vehicles would be helpful for the management of the distresses and for proper conditioning of the road. The aim of the automatic distress condition detection is to save time and resources. To fulfill this aim, development of the detection technique using various interpolation algorithms, mathematical differentiation and image interpretation technique is used as the main tool in this study. The raw data from the laser scanning machine mounted on the vehicle is the basis for the development of the methodology. Using the interpolation functions in the MATLAB platform, visual detection of the cracks is done. The kriging algorithm, delaunay triangulation principle and interpolation techniques and three dimensional plotting were tried and found the best algorithm suitable for the study with regards to the their accuracy of the result towards the reality (based on ocular inspection of the sections interpolated). The interpolated image thus generated were further processed using the differentiation technique which on further filtering and brushing, cracks were sorted out from the non-cracks pixels in the binary image. The cracks pixels and non-cracks pixels were sorted out by image analysis techniques in MATLAB. This analysis generated the image that showed cracks separated from non-crack pixels. By counting of the numbers of the adjacent crack pixels classification of the status of road section was automatically performed. In this study a scale of three classes of presence of cracks was used for classification of the pavement section. The methodology developed in this study however have some limitations related to the classification of the crack classes as potholes, first class, second class and third class. These limitations are basically the length, width and area of cracks. Even, with these limitations, it must be noted that the method developed is promising for the development of automatic distress detection of the pavement. Further enhancement of the method using advance machine and algorithm might be the way ahead of this study. This obviously will help a lot in the real field works if this study could be implemented with some enhancement for the automatic detection and classification of road distress condition.