Most human knowledge is recorded in natural language. The records are kept in computers or onrnpaper to be manipulated and reserved for use in the future. Natural Language processing plays anrnimportant role in increasing computers capability to understand natural languages. Designing andrnimplementing computer programs that can understand natural language is the aim of the works inrnthe area of Natural Language Processing. In order to communicate through natural languagesrngrammatical correctness is very crucial. Therefore, natural language processing applicationsrnshould be enabled to recognize the grammatical errors of natural language texts. This process isrnknown as grammar checking. This work introduces development and design of Amharicrngrammar checker.rnTwo grammar checker approaches have been used in this research. The first approach is a rulebasedrnand it is tested for simple sentences. The rules are constructed manually and matchedrnagainst the patterns of the sentence to be checked. The second approach is statistical approachrnand tested for both simple and complex sentences. In the statistical Amharic grammar checker, ngramrnand probabilistic methods are used to check grammatical errors of Amharic sentence. Thernpatterns and the corresponding probabilities of occurrence are automatically extracted from therntraining corpus and stored in a repository. Sentence probability can be calculated using thesernpatterns and probabilities. Then, probability of the sentence and specified threshold are used torndetermine the correctness of the sentence. The corpus, both for training and test set, is preparedrnfrom a manually part-of-speech text of the language.rnThe evaluation is made in two test cases. The first case is done on simple sentences. In this testrncase, 92.45% precision and 94.03% recall is obtained for the rule-based Amharic grammarrnchecker. On the same test case, the statistical Amharic grammar checker (trigram) showsrnprecision and recall of 67.14% and 90.38% respectively. The statistical Amharic grammarrnchecker is tested using complex sentences in the second test case. In this test case, 63.76% of thernerrors are detected. The evaluation result shows that each approach is capable of detectingrnmultiple errors from a sentence. The false alarms are due to the incomplete grammatical rulesrnand quality of the statistical data. The accuracy of morphological analyzer also affects therngrammar checking result in both approaches.rnKeywords: Statistical grammar checker, rule-based grammar checker, n-gram, POS tag sequence