Current study deals with detecting and ranking the promising regions in a mineral exploration project in order to achieve accurate and reliable results in addition to save energy, time and money. To achieve this goal, two simple, fast and cheap variants of multi criteria decision-making (MCDM) methods called Topsis and Fuzzy-Topsis, have been employed. These MCDM methods are applied on the south and south-east of Sarcheshmeh porphyry copper deposit believed to have great potential of porphyry copper mineralizations. So far, a number of prospects named Darehzar, Sereydoon, northern-Sereydoon, Koohpanj, Dehsiyahan (Bandare Baghoo), Bande Mamzar and Goor-ali Esmaeili has shown promising evidences of porphyry mineralization in this region. In the first step, the promising prospects were determined through integrating all available exploratory data layers including geological lithology and structures, alterations derived out of remotely sensed imagery, geochemical and geophysical anomalies using Fuzzy logic and Weighted overlay methods in an GIS environment. Out of this stage, a number of 20 prospects having at least 2 kilometers area were selected via applying appropriate threshold based on their allocated rank order of fuzzy favorability map. Next the most relevant attributes of the selected promising prospects were selected and averaged over the entire area of those 20 promising areas. Then, the decision-making matrices were computed using these average values over each selected prospect forming the rows of the final matrix having 6 criteria as its columns. These were then inputted to the Topsis and Fuzzy Topsis multi criteria decision making algorithms. In the final processing stage, the Topsis and Fuzzy Topsis algorithm were applied on input matrix to rank the order of best prospects in terms of similarities to porphyry copper mineralization. Based on the final results, the Darehzar prospect with maximum similarty coefficient to the ideal porphyry mineralization ranked first followed by Sereydoon and north-Sereydoon ranking second and third best prospects respectively. Comparing Topsis and Fuzzy-Topsis results show that both methods were capable of prioritizing the promising prospects, however Fuzzy-Topsis gave better ranking order due to having less sensitivity to the noisy input data layers and higher flexibility of weights assigned to the criteria. The validity of the results were
