While miscue detection in general is a well explored research field little attention has so far been paid to miscue detection in dyslexic read speech. This domain differs substantially from the domains that are commonly researched, as for example dyslexic read speech includes frequent regressions and long pauses between words. A system detecting miscues in dyslexic read speech is presented. It includes an ASR component employing a forced-alignment like grammar adjusted for dyslexic input and uses the GOP score and phone duration to accept or reject the read words. Experimental results show that the system detects miscues at a false alarm rate of 5.3% and a miscue detection rate of 40.1%. These results are worse than current state of the art reading tutors perhaps indicating that dyslexic read speech is a challenge to handle.