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Fabric selection is a crucial step in fashion product development. Prior research works have studied the prediction of fabric specimens based on the fabric hand descriptors via either traditional statistical methods or artificial intelligence methods. Despite showing good prediction accuracy, these methods usually lack an understandable ruleset, which means their “interpretability” is low. In this paper, a fuzzy neural network (FNN) based intelligent fabric hand prediction system is explored. Unlike some traditional FNN models in which a full ruleset of the artificial neural network (ANN) is presumed, the proposed FNN system includes a simplification of the network structure and feature selection, so that the number of rules is significantly reduced without big sacrifice on prediction accuracy. Real datasets collected from 30 participants' evaluation on a set of ten fabric specimens are used to train and test the performance of the proposed system. The system's prediction accuracy is found to be over 80%. Applications of the proposed system are discussed and future research directions are outlined.