The paper presents a biometric recognition methodology based on hand thermal information. We start with a hardware presentation, specially designed for this research in a form of thermal sensor plate delivering hand thermal maps, which is a significantly cheaper alternative to thermal cameras. We use a heuristic feature selection technique employing mutual information (mRMR) and well known space transformation methods (PCA and its combination with the LDA) to develop optimal biometric features by selecting those parts of the hand, which deliver the most discriminating personal information. Two different classifiers (k-NN and SVM) are applied and evaluated with a database of hand thermal maps captured for 50 different individuals in three sessions: two at the same day (enrollment attempts), and the third captured a week apart (verification attempt). We achieved 6.67% of an average equal error rate (EER), what suggests that temperature distribution of an inner part of human hand is individual. This may serve as e.g. supporting modality of two-modal biometric recognition (merged with hand geometry or palm print techniques), or may be a good candidate for hand liveness detection approach, as hand thermal maps are difficult to be copied and reconstructed on an artificial object imitating a human hand. To our best knowledge, this is the first work presenting the use of a human hand thermal maps as a direct source of biometric features.