A neural network retrieval method has been applied to investigate AMSU-A/AMSU-B atmospheric temperature and humidity profiling capabilities over land. The retrieval method benefits from a reliable estimate of the land emissivity and skin temperature as well as first guess information regarding the temperature-humidity profiles. It has been applied on a large geographic area (60¿W--60¿E, 60¿S--60¿N) and atmospheric situations (winter and summer). The retrieved RMS errors are within 2 K and 9% in temperature and relative humidity, respectively. Regardless of scanning conditions, vegetation types, and atmospheric situations, the algorithm retrieval results are satisfactory for both temperature and relative humidity. The retrieval approach has been evaluated by comparison with available in situ measurements.