The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially . Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images . Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem . In this paper, a thorough evaluation of eight pre-trained models is presented . Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images . Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate . Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images . The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.