Sign Language is a visual gestural language which is used by deaf people for the purpose ofrncommunication. Even if it is widely used in the deaf community, most of the hearing people dornnot understand it. Due to this communication gap deaf people encounter so many problems inrntheir daily life since they are living with the people who communicate with spoken languages. Tornnarrow this communication gap there should be technological solutions that assist the deafrncommunity. Sign language researches are striving to fill the gap of the communication. The goalrnof this research is the recognition of Isolated Signs in Ethiopian Sign Language.rnThe proposed system accepts videos of Isolated signs and get frames in the videos. On eachrnframe, skin color detection algorithm is applied and the equivalent binary image is producedrnwhich has white value for skin color and black value for other region. Based on the detected skinrnregions the hands and head are segmented from other parts of the body since they have veryrnimportant role in the signing process. Important features are extracted from the segmented bodyrnparts and these values are converted into symbols using k-means algorithm. Hidden MarkovrnModels trained using these symbols and Baum Welch algorithm and stored in the database. ArnViterbi decoding is applied in the recognition process using the trained HMMs and symbolrnsequences of the testing Signs which is prepared by the above process.rnWe evaluated our Isolated Sign Language recognition system and we found a recognition rate ofrn86.9% using 8 features and 83.5% using only 3 features.rnKeywordsrnSign Language, Skin Detection, Hidden Markov Model, Recognition