People who are affected by hearing problems use a communication method called Sign Language. Sign Language differs from region to region, country to country and continent to continent. Machine learning can play a significant role in impacting lives of the hearing-impaired people and can help them to communicate with their environments more easily. This paper presents a very simple and efficient approach for Myanmar Sign Language (MSL) recognition system which is capable of recognizing the static hand gesture images that represent the Myanmar numbers from zero to ten. The main objective of this paper is to investigate the performance of three different Support Vector Machine (SVM) classifiers for Myanmar number sign recognition. The proposed system contains three stages, namely, pre-processing, feature extraction and classification. In the feature extraction stage, different features are extracted using Scale Invariant Feature Transform (SIFT) algorithm. In the classification stage, three different SVM classifiers (SVCs); SVC with linear kernel, SVC with polynomial kernel and LinearSVC are tested and evaluated. Among these three classifiers, SVC with polynomial kernel yielded the highest accuracy score with 87%. Although there are some limitations in the datasets, each classifier provides the encouraging results