Method for operating a cable processing apparatus , comprising the following method steps: - processing (S1) of at least one cable segment (K, K1, K2, K3, Kn), in particular of a cable end, by means of a cable processing unit from an initial configuration to an end configuration (E, E1, E2, E3, En), - capturing (S2) at least one image of the at least one end configuration (E1, E2, E3, En) by means of an image recording device , - applying (S4, S5) a trained neural network to the at least one captured image, wherein the trained neural network is designed, in a first step, to determine (S4) at least one region of interest of the at least one end configuration (E, E1, E2, E3, En) from the at least one captured image and the same neural network is designed, in a second step, to carry out a classification of the at least one determined region of interest with regard to the presence of at least one learned error pattern of the at least one end configuration (E, E1, E2, E3, En) and to determine (S5) a result (A1, A2, A3, A4) assigned to the classification, - generating and outputting (S6) a control signal depending on the at least one determined region of interest and depending on the determined result (A1, A2, A3, A4) of the classification of the at least one end configuration (E, E1, E2, E3, En), - wherein the trained neural network is designed to determine (S3) the complete image capture of an end configuration (E, E1, E2, E3, En), wherein the trained neural network is applied (S3) to the captured image, wherein in the case of the determined incomplete capture of at least one end configuration (E, E1, E2, E3, En), a control signal that causes the notification of an image capture error is generated and output. - processing (S1) of at least one cable segment (K, K1, K2, K3, Kn), in particular of a cable end, by means of a cable processing unit from an initial configuration to an end configuration (E, E1, E2, E3, En), - capturing (S2) at least one image of the at least one end configuration (E1, E2, E3, En) by means of an image recording device , - applying (S4, S5) a trained neural network to the at least one captured image, wherein the trained neural network is designed, in a first step, to determine (S4) at least one region of interest of the at least one end configuration (E, E1, E2, E3, En) from the at least one captured image and the same neural network is designed, in a second step, to carry out a classification of the at least one determined region of interest with regard to the presence of at least one learned error pattern of the at least one end configuration (E, E1, E2, E3, En) and to determine (S5) a result (A1, A2, A3, A4) assigned to the classification, - generating and outputting (S6) a control signal depending on the at least one determined region of interest and depending on the determined result (A1, A2, A3, A4) of the classification of the at least one end configuration (E, E1, E2, E3, En), - wherein the trained neural network is designed to determine (S3) the complete image capture of an end configuration (E, E1, E2, E3, En), wherein the trained neural network is applied (S3) to the captured image, wherein in the case of the determined incomplete capture of at least one end configuration (E, E1, E2, E3, En), a control signal that causes the notification of an image capture error is generated and output. <EOS>