Recently, deep learning (DL) techniques have been gaining interest in the neuroimaging community. In this study, we present 3D convolutional neural network (3D-CNN) as an end-To-end model to label a target task among four sensorimotor tasks for each functional magnetic resonance imaging (fMRI) volume. To the best of our knowledge, this is the first study that employs a single blood-oxygenation-level-dependent (BOLD) fMRI volume as the input of the 3D-CNN for task classification. We hypothesized that 3D-CNN has the capability to extract potentially shift-invariant features in local brain areas while preserving the overall spatial layout of the whole brain fMRI volume. We designed a 3D-CNN model by extending the LeNet-5 CNN for 2D image classification to 3D volume classification. The designed 3D-CNN model was thoroughly evaluated using BOLD fMRI volumes acquired from four sensorimotor tasks in terms of the classification performance and feature representations for each of the four sensorimotor tasks.