A method for analyzing a laser machining process, said method comprising the steps of: - acquiring (S 1) at least one sensor data set for the laser machining process; and characterized by comprising the following: - determining (S2) a value of at least one physical property of a machining result of the laser machining process for quantifying the at least one physical property based on the at least one sensor data set using a transfer function, said transfer function being formed by a trained neural network, wherein the physical property of the machining result is selected from a group comprising a tensile strength, a compressive strength, an electrical conductivity, a keyhole depth, a welding depth, a gap size of a gap between two workpieces joined by the laser machining process, a roughness of a cut edge of a workpiece cut by the laser machining process, a burr of a cut edge of a workpiece cut by the laser machining process, a burr height of a cut edge of a workpiece cut by the laser machining process, a steepness of the cutting front and a perpendicularity of a cut edge of a workpiece cut by the laser machining process, and wherein acquiring (S1) at least one sensor data set is based on a measurement of process radiation of the laser machining process and/or on a measurement of at least one process parameter of the laser machining process, wherein the at least one process parameter comprises a keyhole depth, a focus position, a focus diameter and/or a distance of a laser machining head carrying out the laser machining process from a workpiece . - acquiring (S 1) at least one sensor data set for the laser machining process; and characterized by comprising the following: - determining (S2) a value of at least one physical property of a machining result of the laser machining process for quantifying the at least one physical property based on the at least one sensor data set using a transfer function, - determining (S2) a value of at least one physical property of a machining result of the laser machining process for quantifying the at least one physical property based on the at least one sensor data set using a transfer function, said transfer function being formed by a trained neural network, wherein the physical property of the machining result is selected from a group comprising a tensile strength, a compressive strength, an electrical conductivity, a keyhole depth, a welding depth, a gap size of a gap between two workpieces joined by the laser machining process, a roughness of a cut edge of a workpiece cut by the laser machining process, a burr of a cut edge of a workpiece cut by the laser machining process, a burr height of a cut edge of a workpiece cut by the laser machining process, a steepness of the cutting front and a perpendicularity of a cut edge of a workpiece cut by the laser machining process, and wherein acquiring (S1) at least one sensor data set is based on a measurement of process radiation of the laser machining process and/or on a measurement of at least one process parameter of the laser machining process, wherein the at least one process parameter comprises a keyhole depth, a focus position, a focus diameter and/or a distance of a laser machining head carrying out the laser machining process from a workpiece . <EOS>