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Arabic presents an interesting challenge to natural language processing, being a highly inflected and agglutinative language. In particular, this paper presents an in-depth investigation of the entity detection and recognition (EDR) task for Arabic. We start by highlighting why segmentation is a necessary prerequisite for EDR, continue by presenting a finite-state statistical segmenter, and then examine how the resulting segments can be better included into a mention detection system and an entity recognition system; both systems are statistical, build around the maximum entropy principle. Experiments on a clearly stated partition of the ACE 2004 data show that stem-based features can significantly improve the performance of the EDT system by 2 absolute F-measure points. The system presented here had a competitive performance in the ACE 2004 evaluation.