THE DESIGN OF A RECOGNITION SYSTEM FOR NATURAL OBJECTS IS DIFFICULT, MAINLY BECAUSE SUCH OBJECTS ARE SUBJECT TO A STRONG VARIABILITY THAT CANNOT BE EASILY MODELLED: planktonic species possess such highly variable forms. Existing plankton recognition systems usually comprise feature extraction processing upstream of a classifier. Drawbacks of such an approach are that the design of relevant feature extraction processes may be very difficult, especially if classes are numerous and if intra-class variability is high, so that the system becomes specific to the problem for which features have been tuned. The opposite course that we take is based on a structured multi-layer neural network with no shared weights, which generates its own features during training. Such a large parameterised-fat-network exhibits good generalisation capabilities for pattern recognition problems dealing with position-normalised objects, even with as many as one thousand weights as training examples. The advantage of such large networks, in terms of generalisation efficiency, adaptability and classification time, is demonstrated by applying the network to three plankton recognition and face recognition problems. Its ability to perform good generalisation with few training examples, but many weights, is an open theoretical problem.