On the Internet, where the number of choices is overwhelming, there is a need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem for many Internet users. Recommender systems solve this problem by searching through large volumes of dynamically generated information to provide users with personalized content and services. In view of more personalized clothing requirements, an intelligent clothing recommendation system was designed and developed in this paper. By the use of Transfer Learning to elicit the rich information from the product images, and the use of cosine similarity approach, the user is provided with eclectic recommended products depending on their choices. This was implemented on a web e-commerce application, where users have a wide range to choose their clothing apparel from our database. Only those images are recommended to users which have 80% or above similarity with the product chosen by the user, which helps in solving personalized recommendations.