In real-world scenario, image classification models degrade in performance as the images are corrupted with noise, while these models are trained with preprocessed data. Although deep neural networks (DNNs) are found efficient for image classification due to their deep layer-wise design to emulate latent features from data, they suffer from the same noise issue. Noise in image is common phenomena in real life scenarios and a number of studies have been conducted in the previous couple of decades with the intention to overcome the effect of noise in the image data. The aim of this study was to investigate the DNN-based better noisy image classification system. At first, the autoencoder (AE)-based denoising techniques were considered to reconstruct native image from the input noisy image. Then, convolutional neural network (CNN) is employed to classify the reconstructed image; as CNN was a prominent DNN method with the ability to preserve better representation of the internal structure of the image data. In the denoising step, a variety of existing AEs, named denoising autoencoder (DAE), convolutional denoising autoencoder (CDAE) and denoising variational autoencoder (DVAE) as well as two hybrid AEs (DAE-CDAE and DVAE- CDAE) were used. Therefore, this study considered five hybrid models for noisy image classification termed as: DAE-CNN, CDAE-CNN, DVAE-CNN, DAE-CDAE-CNN and DVAE- CDAE-CNN. The proposed hybrid classifiers were validated by experimenting over two benchmark datasets (i.e. MNIST and CIFAR-10) after corrupting them with noises of various proportions. These methods outperformed some of the existing eminent methods attaining satisfactory recognition accuracy even when the images were corrupted with 50% noise though these models were trained with 20% noise in the image. Among the proposed methods, DVAE-CDAE-CNN was found to be better than the others while classifying massive noisy images, and DVAE-CNN was the most appropriate for regular noise. The main significance of this work is the employment of the hybrid model with the complementary strengths of AEs and CNN in noisy image classification. AEs in the hybrid models enhanced the proficiency of CNN to classify highly noisy data even though trained with low level noise.