Recommender systems are widely used in many domains, especially in E-commerce. It can be used for attracting users by recom- mending appropriate products to them. There are many techniques in recommendation systems which can predict rating scores to recommend next products. In this work, we propose an approach for recommendation based on product images using pre-trained deep learning models and sim- ilarity matching. Specifically, in the proposed model, we have utilized the pre-trained deep learning models (e.g., the VGG16) to extract the im- age features. Then, based on the image features, we compute similarities between the products (e.g. using Cosine similarity). For recommending similar products to the users, we do the same tasks, e.g., extracting fea- ture of the current image, computing its similarity with other images in the database, and generating a list of TOP-N (e.g. TOP-5) most simi- lar products. Experimental results on two public data sets show that the approach can give good recommendations at more than 90% of accuracy.