Automated error detection and feedback systems are becoming an important component of online writing practice services for ESL/EFL (English as a second/foreign language) learners. The main purposes of the study are to: (a) collect samples of essays written by ESL learners with different native language (or L1) backgrounds that are error-coded by an early version of an automated error-detection system (CritiqueTM) and trained human coders; and (b) identify some unique patterns of writing errors for different first language (L1) groups. Data analyzed in this study included 18, 439 TOEFL◯R CBT essays error-coded by CritiqueTM and a much smaller, combined sample of 480 TOEFL◯R CBT/TOEFL iBT◯R essays error-coded by trained human coders. A comparison of error rates across five different language groups showed some unique patterns: (a) the Arabic and Spanish groups were the highest on both spelling and punctuation errors; (b) the Korean and Japanese groups had the highest article error frequency; and (c) the Chinese group had the highest number of errors related to verb conjugations or adjective and noun inflections. The implications of these findings are discussed in terms of understanding the nature of L1-related writing errors and enhancing the automated error detection and feedback systems.