A new computing scheme, pen computing, which includes mobile devices and applications inrnwhich electronic pen along with pen sensitive writing pad is used as the main input tool hasrnbeen emerging. To implement pen-computing applications, online handwriting recognitionrnsystem should be used. Online handwriting recognition engines have been developed forrnvarious character sets. Despite that, no attempt has ever been made to build an onlinernhandwriting recognition engine for Ethiopic character set. Pen-based inputting incorporatedrnwith online handwriting recognition feature allows people to write texts and enter input data inrntheir own natural way of handwriting on an electronic pad.rnThis thesis then is the first attempt to develop an online handwriting character recognitionrnengine for Ethiopic characters. The pen-based devices are evidently unusual in Ethiopia andrnone reason for that is the absence of localized applications. Bringing an online handwritingrnrecognition engine for Ethiopic character set to such devices would play an important role inrnmaking these devices available and usable for the Ethiopian society.rnIn this study, a model for Ethiopic online handwriting character recognition is proposed and arnwriter-dependent online handwriting character recognition engine for the 33+1 basic Ethiopicrncharacters is designed. The designed engine integrates five modules: the data collection andrnpreparation module, the preprocessing module, the feature extraction module, the trainingrnmodule and the classification module. Data collection is done with the aid of digitizerrnsoftware named Neuroscript MovAlyzer, which samples data points along the trajectory of anrninput device (electronic pen or mouse) while the character is drawn. Various algorithms arerndesigned for the preprocessing activities. In the feature extraction module, a new onlinernhandwriting data representation scheme that makes use of the X and Y coordinate observationrncode sequences is proposed. A training algorithm and most importantly a three-layeredrnrecognizer is designed. We are able to show that a reasonably good accuracy is obtained byrnimplementing the proposed algorithms. On the average, a recognition accuracy of up to 99.4%rnis achieved for the sampled two writers. Recognition accuracy 93.4%, 99%, 99.8% are alsornobtained for each of the layers of the recognizer respectively.rnKeywords: Online handwriting recognition, Online handwriting recognition for EthiopicrnCharacters, algorithms for Ethiopic online handwriting recognition, Model forrnEthiopic character online handwriting recognition.