The galaxies, are the systems consisting of stars, gas, dust and dark matter combined with the gravitational force. There are billions of galaxies in the universe. Since the cost of examining each galaxy one by one is high, the classification of the galaxy is an important part of the astronomical data analysis. Galaxies are classified according to morphology and spectral properties. Machine learning methods aimed at revealing the hidden pattern within the data set by analyzing the available data, it can be used to estimate which group of galaxies whose natural groups have not yet been identified. This will save time and cost for both researchers and astronomers. This study has been classified five-variables (Right ascension, Declination, Magnitude, Velocity, and Sigma of Velocity) 4215 galaxies. Galaxies whose natural groups were determined with IDL were classified by using machine learning algorithms with Weka program. Bayes classifier methods, Naive Bayes and Bayes net, Decision tree methods J48, LMT and Random Forest algorithms, Artificial Neural Networks Multilayer Perceptron and Support vector classifier methods were used. The obtained classification results were compared with the natural groups and the predictive performance of the methods were evaluated.