In the recent years, deep learning has shown to have a formidable impact on object classification and has bolstered the advances in machine learning research. Many image datasets such as MNIST, CIFAR-10, SVHN, Imagenet, Caltech, etc. are available which contain a broad spectrum of image data for training and testing purposes. Numerous deep learning architectures have been developed in the last few years, and significant results were obtained upon testing against datasets. However, state-of-the-art results have been achieved through Convolutional Neural Networks (CNN). This paper investigates different deep learning models based on the standard Convolutional Neural Networks and Stacked Auto Encoders architectures for object classification on given image datasets. Accuracy values were computed and presented for these models on three image classification datasets.