We propose a feature reuse approach for an existing multi-layer randomization based feedforward neural network. The feature representation is directly linked among all the necessary hidden layers. For the feature reuse at a particular layer, we concatenate features from the previous layers to construct a large-dimensional feature for the layer. The large-dimensional concatenated feature is then efficiently used to learn a limited number of parameters by solving a convex optimization problem. Experiments show that the proposed model improves the performance in comparison with the original neural network without a significant increase in computational complexity.