This dissertation presents the results of a series of studies on the appearance of manual movements in gestures. The main goal of this research is to increase our understanding of how humans perceive signs and other gestures. Generated insights from human perception may aid the development of technology for recognizing gestures and sign language automatically with cameras and computers. One example of an application of automatic gesture recognition that has played a role in shaping the research in this dissertation is ELo, an Electronic Learning environment for deaf and hearing impaired children to practice Sign Language of the Netherlands (SLN) signs. The questions addressed in the research focus on a number of aspects including temporal processing of signs, discrimination of gestures from other human behaviour, and how humans handle variation in signs.