Research in deep neural network (DNN) and deep learning has great progress for 1D (speech), 2D (image) and 3D (3D-object) recognition/classification problems. As HSI that with 2D spatial and 1D spectral information is quite different from 3D object image, the existing DNN cannot be directly extended to hyperspectral image (HSI) classification. A Multiscale 3D deep convolutional neural network (M3D-DCNN) is proposed for HSI classification, which could jointly learn both 2D Multi-scale spatial feature and 1D spectral feature from HSI data in an end-to-end approach, promising to achieve better results with large-scale dataset. Although without any hand-craft features or pre/post-processing like PCA, sparse coding etc, we achieve the state-of-the-art results on the standard datasets, which shows the technical validity and advancement of our method.