A brain tumor is the irregular development of a mass tissue within the brain or close to it, which has the capacity to spread and duplicate the wildly affecting organs within the body. Brain tumor classification is a vital assignment to assess the tumors and make a treatment choice concurring with their classes. In this study, a model for the classification of brain tumors from MRI images utilizing transfer learning is developed. The convolution neural network (CNN), VGG16 and InceptionV3 techniques are applied to extract features and perform classification with brain MRI image dataset as input. The precision, accuracy, recall, and f1-score metrics are computed to evaluate the performance of the developed model. The results obtained show that the proposed pre-trained model VGG16 achieved better results than other models with an accuracy of 98%.