A machining apparatus for metal, comprising: a gas nozzle for generating a gas jet having a nozzle exit opening at one end on the outside; an electronic camera for acquiring a digital image of the end of the gas nozzle with the nozzle exit opening ; and a pattern recognition module for mapping the digital image onto at least one nozzle pattern from the nozzle state group (109-1, 109-2, 109-3) and nozzle type and being characterised in that the pattern recognition module comprises a trained neural network or a deep learning algorithm and means for detecting the nozzle state and the nozzle type, and wherein digital images obtained by the electronic camera with different illumination of the gas nozzle are used as input data into the input layer of the neural network , and depending on the input images , the nozzle state or the nozzle type of the gas nozzle shown on the digital images is obtained at the output layer of the trained neural network , wherein with one and the same image , both the nozzle type and the nozzle state can be determined; and wherein the neural network is initially trained and taught on the basis of existing image data of used and unused gas nozzles , which have previously been assessed and classified by a cutting expert and wherein the weights between individual neurons and weights of the neurons themselves are set, so that after successful training, the algorithm can automatically detect the nozzle state, in particular, a degree of wear according to the trained criteria and degrees as well as the nozzle type and wherein the respective type and the degree of wear of the gas nozzle are known to the neural network during training. a gas nozzle for generating a gas jet having a nozzle exit opening at one end on the outside; an electronic camera for acquiring a digital image of the end of the gas nozzle with the nozzle exit opening ; and a pattern recognition module for mapping the digital image onto at least one nozzle pattern from the nozzle state group (109-1, 109-2, 109-3) and nozzle type and being characterised in that the pattern recognition module comprises a trained neural network or a deep learning algorithm and means for detecting the nozzle state and the nozzle type, and wherein digital images obtained by the electronic camera with different illumination of the gas nozzle are used as input data into the input layer of the neural network , and depending on the input images , the nozzle state or the nozzle type of the gas nozzle shown on the digital images is obtained at the output layer of the trained neural network , wherein with one and the same image , both the nozzle type and the nozzle state can be determined; and wherein the neural network is initially trained and taught on the basis of existing image data of used and unused gas nozzles , which have previously been assessed and classified by a cutting expert and wherein the weights between individual neurons and weights of the neurons themselves are set, so that after successful training, the algorithm can automatically detect the nozzle state, in particular, a degree of wear according to the trained criteria and degrees as well as the nozzle type and wherein the respective type and the degree of wear of the gas nozzle are known to the neural network during training. <EOS>