Part I. Starting from the properties of networks with backward lateral inhibitions, we define an algorithm for adaptive spatial sampling of line‐structured images. Applications to character recognition are straightforward. Part II. Let be an array of n sensors, each sensitive to an unknown linear combination of n sources. This is a classical problem in Signal Processing. But what is less classical is to extract each source signal without any knowledge either about those signals or about their combination in the sensors outputs. The only assumption is that the sources are independent. This problem emerged from recent studies on neural networks where any message appears as an unknown mixing of primary entities which are to be ‘‘discovered’’. According to the model of neural networks, we propose an algorithm based on: i − a network of fully interconnected processors (like neurons in a small volume of the Central Nervous System). ii − A law which controls the weights of the interconnections, derived from the Hebb concept for ‘‘Synaptic plasticity’’ in Physiology, and very close to the well know ‘‘stochastic gradient algorithm’’ in Signal Processing. This asociation result in a permanent selfearning mechanism which leads to a continuously up‐dating model of the sensor array information structure. After convergence, the algorithm provides output signals directly proportional to the independent primitive source signals.