Method for characterizing a part, comprising the following steps: a) carrying out non-destructive measurements using a sensor, the sensor being placed on the part or facing the part, and being configured to measure a physical quantity allowing a mechanical or thermal characterization of the part; b) using the measurements as input data of a neural network (NN 2, CNN 2), the neural network comprising an input layer, established using the input data, and an output layer, comprising at least one node; c) depending on the value of the node or of each node of the output layer of the neural network, characterizing the part; the method comprising, prior to steps b) and c): - constructing a first database (DB 1), the first database containing measurements of the physical quantity, said measurements being performed or simulated, on a first model part, the first model part being a real part, different from the part to be characterized, or a modelled part, which represents the part to be characterized; - employing a first neural network (CNN 1, CNN' 1, NN 1), the first neural network being parametrized by parameters defined during a first training operation, using the first database (DB 1); the method being characterized in that it also comprises, before steps b) an c): - constructing a second database (DB 2), containing experimental measurements of the physical quantity performed on the part to be characterized; - a second training operation, using the second database (DB 2), so as to parametrize a second neural network (CNN 2, NN 2), the second training operation using parameters of the first neural network (CNN 1, CNN' 1, NN 1) defined in the first training operation; - such that in step c), the neural network used is the second neural network (CNN 2, NN 2), resulting from the second training operation, the second neural network being used to monitor a variation in the state of the characterized part. a) carrying out non-destructive measurements using a sensor, the sensor being placed on the part or facing the part, and being configured to measure a physical quantity allowing a mechanical or thermal characterization of the part; b) using the measurements as input data of a neural network (NN 2, CNN 2), the neural network comprising an input layer, established using the input data, and an output layer, comprising at least one node; c) depending on the value of the node or of each node of the output layer of the neural network, characterizing the part; the method comprising, prior to steps b) and c): - constructing a first database (DB 1), the first database containing measurements of the physical quantity, said measurements being performed or simulated, on a first model part, the first model part being a real part, different from the part to be characterized, or a modelled part, which represents the part to be characterized; - employing a first neural network (CNN 1, CNN' 1, NN 1), the first neural network being parametrized by parameters defined during a first training operation, using the first database (DB 1); - constructing a first database (DB 1), the first database containing measurements of the physical quantity, said measurements being performed or simulated, on a first model part, the first model part being a real part, different from the part to be characterized, or a modelled part, which represents the part to be characterized; - employing a first neural network (CNN 1, CNN' 1, NN 1), the first neural network being parametrized by parameters defined during a first training operation, using the first database (DB 1); the method being characterized in that it also comprises, before steps b) an c): - constructing a second database (DB 2), containing experimental measurements of the physical quantity performed on the part to be characterized; - a second training operation, using the second database (DB 2), so as to parametrize a second neural network (CNN 2, NN 2), the second training operation using parameters of the first neural network (CNN 1, CNN' 1, NN 1) defined in the first training operation; - such that in step c), the neural network used is the second neural network (CNN 2, NN 2), resulting from the second training operation, the second neural network being used to monitor a variation in the state of the characterized part. - constructing a second database (DB 2), containing experimental measurements of the physical quantity performed on the part to be characterized; - a second training operation, using the second database (DB 2), so as to parametrize a second neural network (CNN 2, NN 2), the second training operation using parameters of the first neural network (CNN 1, CNN' 1, NN 1) defined in the first training operation; - such that in step c), the neural network used is the second neural network (CNN 2, NN 2), resulting from the second training operation, the second neural network being used to monitor a variation in the state of the characterized part.