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the process of separating the hand area from a complex background, known as hand segmentation, is a prerequisite for any vision-based hand gesture recognition system. In some applications, only a rough estimate of the hand area is needed, but in other applications an exact segmentation of the hand, if possible, is needed. In this paper, three methods for extracting the hand from the background in real-time were tested. These methods use off-line learning of skin color; these methods are "Histogram Intersection", "Color Histogram", and "Skin Color Modeling and Adaptation". The three methods were tested using a video sequence of 100 frames using four different lighting conditions, with 400 frames in total. The different lighting conditions are fluorescent light only, a mixture of fluorescent and daylight, daylight without the sun light, and daylight with the sun light. These video streams were taken from the same person under the above different lighting conditions. A comparative study between the three methods was performed. The results showed that hand segmentation using "Skin Color Modeling and Adaptation" with Fluorescent light produced the best results among the three methods. The objective of our research is to accurately recognize the hand gestures, and then use it in many different applications. The work in this paper is the first stage in our research. The contribution in this paper is the comparative study between three different techniques that use skin-color modeling under different lighting conditions. Keywords— Hand segmentation, Hand gesture recognition, Histogram intersection, Color Histogram, Skin Color Modeling and Adaptation, Human-computer interaction. Real-time hand area segmentation for hand gesture recognition in desktop environment