In the H2020 CORTEX project, an interdisciplinary team developed neutron noise-based core monitoring techniques implemented as methods and tools based on the approaches of machine learning and artificial intelligence. These methods and tools allow the detection of anomalies in commercial nuclear reactor cores during operation by using the measurements of the fluctuations of the neutron flux – the so-called neutron noise – by very few detectors. The sensitivity of the techniques to changes of different inputs and model parameters were analyzed. Based on these analyses together with the return of experience gained from the operational history of neutron noise measurements, recommendations were derived on how the applicability and the accuracy of the newly developed methods and tools can be improved.