Spellchecking is the process of detecting and providing closer suggestions for incorrectly spelled words with in a text. A spelling error can happen when people use text processing application to produce electronic documents. Based on their context there are two types of spelling error non word and real word errors. Non-word error is an error where the word is misspelled and has no meaning in the language. Real word error is a word that has meaning in the language but contextually incorrect. For Ge’ez language both errors are the major problem in Ge’ez language documents mainly in EOTC we have used Ge’ez for spiritual services the documents are written by Ge’ez. The major issue here is to address non word spelling error problem for Ge’ez language. Currently there is no spellchecker developed for Ge’ez language. In this study, an effective spellchecker system is designed and then implemented using python programming language. The proposed system architecture contains three components preprocessing which is tasked to breaking down a given block of text into tokens, error detection component that verify the validity of words and error correction component which is responsible to provide closer suggestion for the invalid words and three backend components such as Ge’ez words dictionary, morphological rules and Ge’ez character database. Dictionary lookup, N-gram analysis and morphology-based approaches are used for error detection and also for correction. A morphology based approach is appropriate because Ge’ez is morphologically rich language, we have design 1052 morphological rules and suggestions are provided based on Damerau Levenshtin edit distance algorithm. This study follow design science research methodology it entails six steps: problem identification and motivation, setting goals, designing and developing, demonstrating, evaluating, and communicating. The system were evaluated using evaluation metrics according to the experimental results the system achieved Error Recall (84.3%), Error Precision (67.8%), Lexical Recall (95.8%), Lexical Precision (98.3%) and Accuracy (94.7%) for detecting errors. and 76% corrections are generated for correctly identified invalid words. The proposed system mainly focus on typographic errors; so further improvement is essential to expand the work to handle real word errors, and improve morphological rule definitions by adding more word classes. Keywords:-Morphology, Non-word error, error detection, error correction, Spellchecker.