Self-Organizing Maps are popular tools for data visualization and clustering. At the same time, due to the incorporation of new transactions, real-life databases change periodically. As a consequence of changes in our databases; our maps, which are derived from them, often become outdated and are therefore no longer usable for decision support. To tackle this problem, the application of incremental training methods has been suggested. The current incremental methods have been developed based on non-batch procedures. In this work, a batch-incremental-training algorithm for a self-organizing map is proposed. The results obtained are promising enough to affirm that the batch method might be considered for non-stationary environments.