A method, performed by a fault localization system , for predicting a location of a fault in a computer system , the method comprising: receiving a sample of machine-level metrics relating to the computer system , mapping the sample to an initial unit of the SSOM; obtaining a faulty unit pertaining to a Supervised Self-Organizing Map, SSOM, and wherein the faulty unit has been derived from a sample of machine-level metrics relating to the computer system , wherein the SSOM has been trained with a first layer (X) of machine-level metrics and a second layer (Y) of service-level metrics, wherein the trained SSOM includes at least one faulty unit and at least one normal unit, wherein the method is characterized by : expanding a radius of a circle originating at the faulty unit until a predefined number (Q) of normal units of the SSOM falls within the circle, computing , for each machine-level metric, a respective score based on differences between the faulty unit and the predefined number (Q) of normal units, wherein a set of scores comprises the respective score for each machine-level metric, and selecting a sub-set of the set of scores, wherein the sub-set comprises a number (k) among the greatest scores of the set of scores, whereby the location of the fault is predicted to be located to one or more machine-level metrics represented by the sub-set of scores. receiving a sample of machine-level metrics relating to the computer system , mapping the sample to an initial unit of the SSOM; obtaining a faulty unit pertaining to a Supervised Self-Organizing Map, SSOM, and wherein the faulty unit has been derived from a sample of machine-level metrics relating to the computer system , wherein the SSOM has been trained with a first layer (X) of machine-level metrics and a second layer (Y) of service-level metrics, wherein the trained SSOM includes at least one faulty unit and at least one normal unit, wherein the method is characterized by : expanding a radius of a circle originating at the faulty unit until a predefined number (Q) of normal units of the SSOM falls within the circle, computing , for each machine-level metric, a respective score based on differences between the faulty unit and the predefined number (Q) of normal units, wherein a set of scores comprises the respective score for each machine-level metric, and selecting a sub-set of the set of scores, wherein the sub-set comprises a number (k) among the greatest scores of the set of scores, whereby the location of the fault is predicted to be located to one or more machine-level metrics represented by the sub-set of scores. expanding a radius of a circle originating at the faulty unit until a predefined number (Q) of normal units of the SSOM falls within the circle, computing , for each machine-level metric, a respective score based on differences between the faulty unit and the predefined number (Q) of normal units, wherein a set of scores comprises the respective score for each machine-level metric, and selecting a sub-set of the set of scores, wherein the sub-set comprises a number (k) among the greatest scores of the set of scores, whereby the location of the fault is predicted to be located to one or more machine-level metrics represented by the sub-set of scores. <EOS>