Laser photoablation using UV or IR lasers is known to trigger a photo acoustic event. Different target materials can be discriminated by analyzing the photoacoustic signal. In vitro measurements of NCPAS have revealed the necessity to integrate a number of variable elements (tissue hydration, fluence, laser beam diameter, distance to microphone, etc.). This study defines the parameters needed for the initiation of a self learning system for target material recognition. A UV excimer laser (λ=193 nm; Summit Technology, UV 200L) was used to ablate organic polymers (PMMA, PA, PVC), normal and porcine corneal scar tissue, and human cornea in vivo. NCPAS was performed using a microphone (up to 200 kHz) as a detector. During photoablation, the acoustic signal was analyzed by a multiport A/D IBM PC based digital oscilloscope. The data obtained were imported into a Matlab language program and analyzed. NCPAS allows the discrimination of different materials by a characteristically frequency shift of the photoacoustic signal. Detection and online material recognition using NCPAS is a step on the way to a `smart' laser control, based on an artificial neuronal network.