The correct identification of minerals is crucial task for the exploration and exploitation of mineral resources, environmental monitoring, and industrial processes. In this article, we propose a hyperspectral imaging system and classification model to identify nine types of minerals. To accomplish this, we employed a hyperspectral shortwave infrared (SWIR) camera to capture hyperspectral images. We then introduce a convolutional neural network (CNN) architecture that considers only spectral data, complemented by a fully connected network for classification. To prevent overfitting, we implemented the dropout technique, which enables random deactivation of neurons during the backpropagation process. This results in improved performance during the training phase and a better generalization capacity. Training was optimized to minimize the categorical cross-entropy objective function, and the model was evaluated during training using an accuracy metric. Finally, we evaluated the results with the test data using accuracy, recall, and precision metrics, and achieved 98.52%, 98.25%, and 98.68%, respectively. Our source code is available at https://github.com/jcifuenr/Spec-CNN.