The unique characteristics of Ethiopic script unlike other scripts (like English), it has a characteristics of large number of class, and confusion between similar characters. In this work, the Ethiopic online handwriting system using hybrid feature extraction technique model is presented. A system is developed based on the nature of Ethiopic scripts and the information collected from online handwriting data collection. In this approach, the data is collected by a lipi toolkit and using a mobile phone, Tablet PC, and touch enabled laptops from 80 different writers. Then the preprocessing module will minimize and remove the noises in the data collected. In addition to important features like Linearity, curliness, curvature, zones, aspects, the feature of spatial distribution of the strokes in a character and the sequence and number of strokes a character comprises will be extracted to the feature vector for classification using SVM. As a classifier we use a multi-class and polynomial kernel SVM algorithms. The recognition system is tested with the collected dataset and experimental results are reported. A dataset collected for training and testing online recognition systems for 238 Ethiopic scripts. The dataset is prepared in accordance with the international standard UNIPEN format. The recognition system is tested with the collected dataset and the proposed system achieved character recognition rates of 94% for Ethiopic scripts. Application of such recognition systems may be as personal digital assistants, smart phones, computer-aided education.