A new artificial neural network (ANN)-based approach has been proposed in this paper to recognise handwritten digits. Handwritten digit recognition finds its applications in many areas of computer vision and artificial intelligence. The proposed ANN has a logical framework of five levels. Three hidden layers independently capture the features of a digit; then associative relationship among the features followed by the possible forms of a handwritten digit. The performance of the neural network is analysed by varying the number of nodes in these three layers. It is further suggested to pre-process the data to avoid the problem of overfitting in which case the noise is incorporated into the model instead of the signal. The data are pre-processed for removing white spaces outside the boundary of a digit's image, considering them as noise. In addition, the dropout strategy of Srivastava et al. (2014) has also been implemented, resulting in a better accuracy at a cost of about 18% of extra CPU time. Finally, the optimised size of the neural network with the proposed architecture is also determined to yield the best performance. The performance of the proposed architecture was found to be very close to that of Srivastava et al. (2014), but comparatively very small in size and requiring much less CPU time.