Method for operating a surgical microscope, wherein a left-side image representation of a capture region of the surgical microscope is captured by means of a left-side camera of the surgical microscope, wherein a right-side image representation of the capture region is captured by means of a right-side camera of the surgical microscope, wherein the captured left-side image representation and the captured right-side image representation are fed as input data to at least one trained machine learning method and/or computer vision evaluation method that is made available by means of a control device of the surgical microscope, and wherein, by means of the at least one trained machine learning method and/or computer vision evaluation method and by using the captured image representations (211, 21r) as a starting point, a surgical target object in the captured image representations (211, 21r) is identified, wherein a type of the target object and a position of the target object are determined in the image representations (211, 21r) during the identification of the target object, and wherein optimum microscope parameters and/or a change in microscope parameters and/or control commands for an actuator system of the surgical microscope are estimated for the identified target object, wherein control commands for the actuator system of the surgical microscope are generated by means of the control device from the estimated optimum microscope parameters and/or the estimated change in microscope parameters, and/or wherein the actuator system is controlled according to the generated and/or estimated control commands, with the result that the microscope parameters for the surgical microscope are configured optimally with regards to the identified surgical target object, wherein the at least one trained machine learning method and/or computer vision evaluation method estimates and provides a confidence value for each estimated output, wherein the output comprises the identified surgical target object, wherein the confidence value provided is compared with a specified threshold value, wherein the method is aborted if the confidence value provided is below the specified threshold value. wherein a right-side image representation of the capture region is captured by means of a right-side camera of the surgical microscope, wherein the captured left-side image representation and the captured right-side image representation are fed as input data to at least one trained machine learning method and/or computer vision evaluation method that is made available by means of a control device of the surgical microscope, and wherein, by means of the at least one trained machine learning method and/or computer vision evaluation method and by using the captured image representations (211, 21r) as a starting point, a surgical target object in the captured image representations (211, 21r) is identified, wherein a type of the target object and a position of the target object are determined in the image representations (211, 21r) during the identification of the target object, and wherein optimum microscope parameters and/or a change in microscope parameters and/or control commands for an actuator system of the surgical microscope are estimated for the identified target object, wherein control commands for the actuator system of the surgical microscope are generated by means of the control device from the estimated optimum microscope parameters and/or the estimated change in microscope parameters, and/or wherein the actuator system is controlled according to the generated and/or estimated control commands, with the result that the microscope parameters for the surgical microscope are configured optimally with regards to the identified surgical target object, wherein the at least one trained machine learning method and/or computer vision evaluation method estimates and provides a confidence value for each estimated output, wherein the output comprises the identified surgical target object, wherein the confidence value provided is compared with a specified threshold value, wherein the method is aborted if the confidence value provided is below the specified threshold value.