Polar-orbiting meteorological satellites collect imagery across a wide range of solar illumination and atmospheric conditions. Algorithms used to create automated cloud analyses from these data must compensate for variations in cloud signatures caused by changes in atmospheric attenuation and solar scattering geometry that occur as the satellite orbits the Earth. In this paper, a methodology is presented that describes the variations in cloud spectral signatures that result from changes in satellite observational conditions. Relationships are developed that describe the impact of solar illumination and scattering geometry on the spectral signature of optically-thick water clouds in the daytime Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared imageryxs. Additional relationships are presented that describe the impact of total integrated water vapour on the spectral signatures of optically-thin cirrus and stratus clouds in night-time AVHRR infrared. Threshold functions are then derived from these relationships and demonstrated in the automated analysis of high resolution AVHRR imagery. The accuracy of each automated analysis is measured against a ground truth (manual) cloud-no-cloud analysis created from the multi-spectral imagery. It is concluded that highly accurate automated cloud analyses are achievable using bi-spectral cloud detection techniques that employ the threshold function methodology to compensate for global variations in cloud spectral signatures.