Method for determining the performance of a welding process carried out on at least one metal part, especially an electric arc-welding or laser-welding process, said method comprising the following steps: a) acquiring, with an image-capturing device , at least one initial image of at least one surface segment of said part, this step being carried out on said part previously welded comprising a weld bead , once welding of said part has finished, confirmed spatters being located on the surface of said part, b) carrying out a first digital processing operation on the initial image so as to locate, in said initial image , the presumed spatters , characterized in that it further comprises the following steps: c) inputting one or more extracts from the initial image , each comprising one presumed spatter , into at least one neural network , in particular a convolutional neural network, so as to classify the presumed spatters into confirmed spatters or unconfirmed spatters, d) carrying out a second digital processing operation on the initial image comprising the spatters classified confirmed in step c) so as to determine at least one parameter representative of the quantity of confirmed spatters chosen from: - the area of one or more confirmed spatters , - the total area of the confirmed spatters , which is defined as the sum of the areas of each confirmed spatter , - the number of confirmed spatters , - the number of confirmed spatters per unit area, - the spatter density, defined as the total area of confirmed spatters divided by the total area (S 2 ) of the initial image , - the average of the distances between each confirmed spatter and the weld bead , e) determining the performance of the welding process on the basis of the at least one parameter determined in step d). a) acquiring, with an image-capturing device , at least one initial image of at least one surface segment of said part, this step being carried out on said part previously welded comprising a weld bead , once welding of said part has finished, confirmed spatters being located on the surface of said part, b) carrying out a first digital processing operation on the initial image so as to locate, in said initial image , the presumed spatters , c) inputting one or more extracts from the initial image , each comprising one presumed spatter , into at least one neural network , in particular a convolutional neural network, so as to classify the presumed spatters into confirmed spatters or unconfirmed spatters, d) carrying out a second digital processing operation on the initial image comprising the spatters classified confirmed in step c) so as to determine at least one parameter representative of the quantity of confirmed spatters chosen from: - the area of one or more confirmed spatters , - the total area of the confirmed spatters , which is defined as the sum of the areas of each confirmed spatter , - the number of confirmed spatters , - the number of confirmed spatters per unit area, - the spatter density, defined as the total area of confirmed spatters divided by the total area (S 2 ) of the initial image , - the average of the distances between each confirmed spatter and the weld bead , - the area of one or more confirmed spatters , - the total area of the confirmed spatters , which is defined as the sum of the areas of each confirmed spatter , - the number of confirmed spatters , - the number of confirmed spatters per unit area, - the spatter density, defined as the total area of confirmed spatters divided by the total area (S 2 ) of the initial image , - the average of the distances between each confirmed spatter and the weld bead , e) determining the performance of the welding process on the basis of the at least one parameter determined in step d). <EOS>