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This paper is concerned with the automatic analysis of data coming from the multidetector array CHIMERA, used in nuclear physics at intermediate energies. Each of Chimera's detection cells is a telescope made of a ΔE silicon detector and a CsI(Tl) crystal, thick enough to stop all the charged light particles. The signals produced in the CsI(Tl) scintillators can be subdivided into two components-fast and slow. These data are collected in the form of bi-dimensional matrices (Fast-Slow matrices), particularly important for light particle identification. The proposed approach consists in applying image processing techniques. In particular, Grossberg's pre-attentive neural networks are used as a first step in order to isolate the regions of physical interest in the matrices and to roughly identify the directions depicted by the most intense lines; a successive step of filtering based on Markov random fields is then performed.