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Document clustering has been an important research area in recent years. However, most work on this subject has focused on batch processing in a static environment. For real world applications, online and incremental processing of highly dynamic data is required. Adaptive resonance theory (ART) neural networks possess several interesting properties that make them appealing as a potential solution to this problem. In this paper, we present preliminary experimental results that examine ART text clustering under several situations characteristic of real-life applications. We also compare our present results with work we have conducted previously on the batch static case, hence determining how clustering quality is affected by incremental processing.