We can recognize the emotion of a human by seeing their facial expression and it is an efficient way of human communication. It is the easiest way and essential technology for realizing the human and machine interaction. Facial expression recognition task can be able to classify the face images into various categories of emotions such as happy, sad, angry, fear, surprise, disgust and neutral. In this paper, we are analysing and efficiently classifying each facial image into one of the emotion category. There are numerous approaches to address and solve this problem, out of them convolutional neural network (CNN) is the best approach. Here, we are proposing a novel technique called facial emotion recognition using convolutional neural networks. It is based on the feature extractor to extract the feature and the classifier to produce the label based on the feature. The extraction of feature may be imprecise by variance of location of object and lighting condition on the image. The feature of image can be extracted without user defined feature engineering, and classifier model is integrated with feature extractor to produce the result when input is given. In this way, the CNN approach can produces a feature location invariant image classifier that achieves higher accuracy than conventional linear classifier and our model classified the emotions with 66.62 accuracy.