The deep convolutional neural network (CNN) has recently attracted the researchers for classification of hyperspectral remote sensing images. The CNN mainly consists of convolution layer, pooling layer and fully connected layer. The pooling is a regularisation technique and improves the performance of CNN while reducing the computation time. Various pooling strategies have been developed in literature. This study shows the effect of pooling strategy on the performance of deep CNN for classification of hyperspectral remote sensing images. The authors have compared the performance of various pooling strategies such as max pooling, average pooling, stochastic pooling, rank-based average pooling and rank-based weighted pooling. The experiments were performed on three well-known hyperspectral remote sensing datasets: Indian Pines, University of Pavia and Kennedy Space Center. The proposed experimental results show that max pooling has produced better results for all the three considered datasets.