A generic method for target recognition is presented. The stress is put on the methods based on the neural networks and more specifically on the adaptive resonance theory (ART) models. This type of artificial neural network (ANN) has the advantage of being unsupervised and adaptive: it is indeed able to acquire and adapt its long-term memory taking into account the context evolution. ART networks very quickly recognize classes that are already known, they also learn new images very fast. Two versions of ART are investigated: ART1, which only works with binary data, and ART2, which is working with analog data. In practice, ART1 seems to need larger images than ART2 to achieve the same efficiency, but is obviously faster. A preprocessor has been developed whose output is invariant to translation, rotation, and scale changes of the input. The most important feature of this preprocessor is its ability to preserve visual interpretation, which is not the case for the more classical methods using Fourier-like and log/polar transforms.