In this work we propose two parallel algorithms, for image compression, based on multilayer neural networks, by subdividing the image into blocks. The first parallel technique is based on a static distribution of blocks to processors. The advantage of this distribution is that the training phase (construction of the compressor-decompressor network) does not need any communication but its drawback is the load balancing problem. The second parallel technique improves the load balancing problem by using a dynamic distribution of blocks but it requires communication between processors. These two implementations are tested and compared on a distributed memory machine under PVM.