The excavation of rock masses has a crucial importance for the safety and cost of construction of engineering projects. In this study, a flexible excavation assessment (EXCASS) system was developed with two components, the geological strength index (GSI) and the point load strength index (I-s50). In order to develop the system, artificial neural network (ANN) analysis were performed using datasets from surface excavations (12 sites in Turkey and 61 sites in Greece reported by Tsiambaos and Saroglou (2010)). The EXCASS system in this study includes two parameters, namely the excavation power index (EPI) and the excavation performance rating (EPR) ranging between the value of 0 and 100. The optimum excavation power index (EPIopt) can be determined theoretically by using (GSI(2) x root Is(50)) as an input when EPR is equal to 0, for the selection of the optimum excavation method. EPR is a rating which provides relative information on the ease or difficulty of the excavation method. Although the value of EPR varies between 0 and +/- 100 in the EXCASS system, the use of the excavation method for "normally classes" is recommended in order to achieve costly and efficient excavation. The EXCASS system, on the other hand, has an ability to respond to possible technological developments by associating the excavation method with the EPI rating.