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This paper introduces an approach called Clustering and Co-evolution to Construct Neural Network Ensembles (CONE). This approach creates neural network ensembles in an innovative way, by explicitly partitioning the input space through a clustering method. The clustering method allows a reduction in the number of nodes of the neural networks that compose the ensemble, thus reducing the execution time of the learning process. This is an important characteristic especially when evolutionary algorithms are used. The clustering method also ensures that different neural networks specialize in different regions of the input space, working in a divide-and-conquer way, to maintain and improve the accuracy. Besides, the clustering method facilitates the understanding of the system and makes a straightforward distributed implementation possible. The experiments performed with seven classification databases and three different co-evolutionary algorithms show that CONE considerably reduces the execution time without prejudicing (and even improving) the accuracy, even when a distributed implementation is not used.