Microarray data collects information from tissues that could be used in early diagnosis such as cancer. However, the classification of microarray data is a challenging task due to the high number of features and a small number of samples leading to poor classification accuracy. Feature selection is very effective in reducing dimensionality; it eliminates redundant and irrelevant features to enhance the classifier’s performance. In order to shed light on the strengths and weaknesses of the existing techniques, we compare the performances of five embedded feature selection methods namely decision tree, random forest, lasso, ridge, and SVM-RFE. Ten well-known microarray datasets are tested. Obtained results show the outperformance of SVM-RFE in term of accuracy, and comes in the second position after decision tree in terms of number of selected features and execution time.