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A distance-based classifier for Arabic text categorization was proposed. The classifier, in its learning phase, scans the set of training documents once to extract features of categories that capture inherent category-specific properties; while in its testing phase the classifier uses category-specific features to categorize unclassified documents. Stemming was used to reduce the dimensionality of feature vectors. The accuracy of the classifier was tested by carrying out several categorization tasks on an in-house collected Arabic corpus. The results show that the proposed classifier is very accurate and robust.