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A new concept for cloud detection from observations by multispectral spaceborne imagers is proposed, and an algorithm comprising many pixel-by-pixel threshold tests is developed. Since in nature the thickness of clouds tends to vary continuously and the border between cloud and clear sky is thus vague, it is unrealistic to label pixels as either cloudy or clear sky. Instead, the extraction of ambiguous areas is considered to be useful and informative. We refer to the multiple threshold method employed in the MOD35 algorithm that is used for Moderate Resolution Imaging Spectroradiometer (MODIS) standard data analysis, but drastically reconstruct the structure of the algorithm to meet our aim of sustaining the neutral position. The concept of a clear confidence level, which represents certainty of the clear or cloud condition, is applied to design a neutral cloud detection algorithm that is not biased to either clear or cloudy. The use of the clear confidence level with neutral position also makes our algorithm structure very simple. Several examples of cloud detection from satellite data are tested using our algorithm and are validated by visual inspection and comparison to previous cloud mask data. The results indicate that our algorithm is capable of reasonable discrimination between cloudy and clear-sky areas over ocean with and without Sun glint, forest, and desert, and is able to extract areas with ambiguous cloudiness condition.