Recognition of facial expressions plays a major role in many automated system applications like robotics, education, artificial intelligence, and security. Recognizing facial expressions accurately is challenging. Approaches for solving FER (Facial Expression Recognition) problem can be categorized into 1) Static single images and 2) Image sequences. Traditionally, different techniques like Multi-layer Perceptron Model, k-Nearest Neighbours, Support Vector Machines were used by researchers for solving FER. These methods extracted features like Local Binary Patterns, Eigenfaces, Face-landmark features, and Texture features. Among all these methods, Neural Networks have gained very much popularity and they are extensively used for FER. Recently, CNNs (Convolutional Neural Networks) have gained popularity in field of deep learning because of their casual architecture and ability to provide good results without requirement of manual feature extraction from raw image data. This paper focuses on survey of various face expression recognition techniques based on CNN. It includes state-of-the-art methods suggested by different researchers. The paper also shows steps needed for usage of CNN for FER. This paper also includes analysis of CNN based approaches and issues requiring attention while choosing CNN for solving FER.