Introduction: The brain is the major and vital organ of the central nervous system. After the age of 60 or in old age, the human brain may suffer from various disorders. Brain diseases may also occur due to some inevitable causes in the normal human body. As the brain stops functioning, the human body goes into a paralyzed state. To treat various brain diseases, neurologists use different brain imaging techniques. Aims: Magnetic Resonance Imaging (MRI) technique is one of the promising imaging techniques used in recent days for analyzing brain diseases. Manual analysis and classification of brain images into normal or deceased is a tedious task. So different supervised learning techniques are used for this purpose. Methodology: This paper focuses on the experimental study on feature selection using PCA and LDA and classification of two of the brain image datasets i.e. Glioma and Alzheimer. Result: The experimental study suggested that the PCA+MLP classifier obtained accuracy values of 92.68% for the Glioma dataset and 90.023% for the Alzheimer dataset. PLC is used for feature reduction and MLP is used as a classification task. Conclusion: The results suggested that PCA with MLP outperformed the other models.