In order to evaluate the seismic vulnerability at large scale, it is necessary to gain awareness of the soil properties, types of materials used in the construction of buildings during different periods, the construction standards of each period, different techniques of construction, the epicentre of the earthquake, etc. An ideal evaluation would take into account all the mentioned variables above; however, in this study only the building typologies are considered (Classification according to EMS-98). More precisely, the typologies are identified for a small data set manually. Each building possesses four attributes such as: 1) construction period, 2) number of floors, 3) surface of the building, and 4) Roof shape. In order to obtain the attributes above, data sets from Statistic Federal Office (OFS) and territorial information system in Geneva (SITG) were acquired. In addition, these data which embodied many imprecisions as well as irregularities were examined and treated. Furthermore, machine learning techniques are applied and will result in the acquisition of the building typologies for the whole city of Geneva. These machine learning methods are trained on a learning set collected manually, and then applied to the testing set which corresponds to the whole attribute combinations of Geneva. All in the interest of obtaining the corresponding typologies.