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This paper presents a novel sensor for classification of material type and its surface roughness. The sensor is developed by means of a lightweight plunger probe and an optical mouse. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The time and features of bouncing signal are related to the material type and its surface properties, and each material has a unique set of such properties. During the bouncing of the probe, a time varying signal is generated from optical mouse that is recorded in a data file on PC. Some dominant unique features are then extracted using digital signal processing tools to optimize neural network based classifier used with the sensor. The classifier is developed on the basis of application of supervised structures of neural networks. For this, an optimum multilayer perceptron neural network (MLP NN) model is designed to maximize accuracy under the constraints of minimum network dimension. The optimal parameters of MLP NN model based on various performance measures and classification accuracy on the testing datasets even after attempting different data partitions are determined. The classification accuracy of MLP NN is found reasonable consistently in respect of rigorous testing using different data partitions. The performance of the proposed MLP NN based classifier has also been compared with the statistical classification trees approach. It is seen that the former one clearly outperforms the statistical approach.