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An analog neural network (NN) was designed, implemented in 0.35-mum complementary metal-oxide-semiconductor technology, and tested to increase the distance measurement interval of a phase-shift laser rangefinder and to classify different types of surfaces for varying distances and incidence angles. This paper focuses on the ability of the NN to remove the indecision on the distance value deduced from the phase-shift measurement. The NN architecture is a multilayer perceptron (MLP) with two inputs, three processing neurons in the hidden layer, and one output neuron. The amplified and filtered photoelectric signal provided by the rangefinder is set at one input. The NN is trained so that its output voltage is proportional to the distance for easy evaluation. By combining both measurements coming from the rangefinder and the NN, it is possible to obtain a resolution of 50 mum on a distance interval [0.5 m; 3.2 m], whereas the rangefinder measurement range width is limited to 0.9 m. This paper presents the complete system, concentrating more on the training phase of the implemented NN and on the experimental results.