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Automation of oral reading assessment and of feedback in a reading tutor is a very challenging task. This paper describes our research aiming at developing such automated systems. First topic is the recording and annotation of CHOREC, the Flemish database of childrens oral reading we develop in order to characterize oral reading processes statistically. Next, we propose a classification of both oral reading strategies and errors, which provides the basis of the envisaged assessment and feedback. Finally, experimental results show that our two-layered recognition system is able to provide high reading miscue detection rates, while only few correctly read words are erroneously tagged as miscue.