In this paper we have developed a new distance measurement method which can obtain distance information directly from the output waveform of pulsed laser radar (PLR). A simple digital signal processing technique and multi layer perceptrons (MLP) type neural network (NN) have been used to recognize the pulse shapes and to obtain the distance information. The method has been implemented in a real PLR to make possible very short distance measurements with overlapping of reference and reflected pulses. Because of the ability of NNs in extracting the information from nonlinear and noisy data and preprocessing of the noisy input pulse shapes to the NN, a distance standard deviation of 0.3 mm and average nonlinearity error of 2.14 mm are achieved. Because in this method the effect of the noise is decreased, it is possible to make the measurements with the same resolution as in standard method with no overlapping but with lower averaging in sampling unit and this dramatically increases speed of the measurements.

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