Multimodal emotion recognition is a challenging task due to the various modalities that emotions can be expressed with. It has applications in various domains, including human-computer interaction, multimedia retrieval, healthcare, crime, etc. We propose a multimodal emotion recognition system which is based on body language, facial expression and speech data. This presents the details of the techniques used in the Multimodal Emotion Recognition in Polish challenge. To capture the emotional content for various styles of videos, data preprocessing operations need to be performed and robust features are extracted. For this purpose, we have used facial landmark detection for facial expressions and MFCC for speech. The data, in the form of videos, had variable length. To tackle this challenge, we used a long short-term memory network. Each of the modalities are trained using a combination of LSTM and dense layers and return an emotion with the highest probability. Then the models are combined using a weighted average approach, where the emotion with highest probability is considered