In this contribution an image classification by uncoupled Cellular Nonlinear Networks (CNN) is proposed and evaluated on typical datasets, like CIFAR-10 and MNIST. The algorithm is based on the application of backpropagation for the training of synaptic coupling weights and is capable of binary classification by means of a threshold-based classifier. The design is inspired by recent deep neural network architectures, but can be implemented on a CNN Universal Machine, enabling complex image recognition on low-power embedded devices.