MRI is favorable for brain imaging due to its excellent soft tissue contrast and absence of harmful ionizing radiation. Many have proposed supervised multimodal neural networks for automatic brain tumor segmentation and showed promising results. However, they rely on large amounts of labeled data to generalize well. The trained network is also highly specific to the task and input. Missing inputs will most likely have a detrimental effect on the network’s predictions, if it works at all. The aim of this thesis work is to implement a deep neural network that learns the general representation of multimodal MRI images in an unsupervised manner and is insensitive to missing modalities. With the latent representation, labeled data are then used for brain tumor segmentation. A variational autoencoder and an unified representation network are used for repre- sentation learning. Fine-tuning or joint training was used for segmentation task. The performances of the algorithms at the reconstruction task was evaluated using the mean- squared error and at the segmentation task using the Dice coefficient. Both networks demonstrated the possibility in learning brain MR representations, but the unified representation network was more successful at the segmentation task.