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Validation of forensic voice comparison methods requires testing using speech samples that are representative of forensic casework conditions. Increasingly, around the world, forensic voice comparison casework is being undertaken using automatic speaker recognition (ASR) systems. However, multilingualism remains a key issue in applying automatic systems to forensic casework. This research aims to consider the effect of language on ASR performance, testing developers' claims of 'language independency'. Specifically, we examine the extent to which language mismatch either between the known and questioned samples, or between the evidential samples and the calibration data, affects overall system performance and the resulting strength of evidence (i.e., likelihood ratios for individual comparisons). Results indicate that mixed language trials produce more errors than single language trials which makes drawing evidential conclusions based on bilingual data challenging.