Sign language is the basic way to communicate and understand ideas among people with disabilities, deaf and dumb, and to recognize and understand the meanings of the motions and the gestures used by them that requires knowledge of certain features such as hand position, form, motion, directions and facial expressions. Moreover, the extraction of image features is important and it plays a vital role in further analysis of image processing. Extracting the special features of the isolated Arabic sign language (ArSL) words based on Discrete Cosine Transform (DCT) was proposed in current paper. By monitoring and tracking the trajectory of the hands when single hand movement or double hands extract the required features. That is the independent sign language word features vector is incorporated as an input into the classification stage to derive the meaning of the word as voice or text. The research data were collected in cooperation with the Ministry of Labor and Social Affairs of Iraq with the assistance of the Special Needs Department and up to 40 words of isolated sign language collected. Two signs were taken as examples of feature extraction (Happy and water). Since the word happy gives an example of a sign word performed by two hands, on the other hand, the word water is performed with one hand, the Arabic sign language contains different words, part of them are performed with one hand and the other part with both hands.