The RNAstrand tool presented in this contribution uses a SVM to predict strand information from a set of four thermodynamic features that can readily be computed for any multiple sequence alignment based on well-established energy parameters and dynamic programming algorithms. We show here that, together with basic information on the size, sequence and GU base pair variation in the input alignment, these features are sufficient to determine the reading direction of an RNA motif with an evolutionary conserved secondary structure. The tool RNAstrand achieves classification accuracies of 90% and above for most ncRNA families. On microRNAs, its performance is comparable to that of EvoFold. In applications to data from organisms for which not much genomic DNA has been sequenced, RNAstrand has the advantage that it does not require fairly accurate estimates of evolutionary distances as input.