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Patterns, types, and causes of errors in children’s pronunciation can be more variable than in adults’ speech. In school settings, different specialists work with children depending on their needs, including speech-language pathology (SLP) professionals and English as a second language (ESL) teachers. Because children’s speech is so variable, it is often difficult to identify which specialist is better suited to address a child’s needs. Computers excel at pattern recognition and can be quickly trained to identify a wide array of pronunciation issues, making them strong candidates to help with the difficult problem of identifying the appropriate specialist. As part of a larger project to create an automated pronunciation diagnostic tool to help identify which specialist a child may need, we created a pronunciation test for children between 5 and 7 years old. We recorded 26 children with a variety of language backgrounds and SLP needs and then compared automatic evaluations of their pronunciation to human evaluations. While the human evaluations showed high agreement, the automatic mispronunciation detection (MPD) system agreed on less than 50% of phonemes overall. However, the MPD showed consistent, albeit low, agreement across four subgroups of participants with no clear biases. Due to this performance, we recommend further research on children’s pronunciation and on specialized MPD systems that account for their unique speech characteristics and developmental patterns.