Since there are no systematic pauses delimiting words in speech, the problem of word segmentation is formidable even for monolingual infants. We use computational modeling to assess whether word segmentation is substantially harder in a bilingual than a monolingual setting. Seven algorithms representing different cognitive approaches to segmentation are applied to transcriptions of naturalistic input to young children, carefully processed to generate perfectly matched monolingual and bilingual corpora. We vary the overlap in phonology and lexicon experienced by modeling exposure to languages that are more similar (Catalan and Spanish) or more different (English and Spanish). We find that the greatest variation in performance is due to different segmentation algorithms and the second greatest to language, with bilingualism having effects that are smaller than both algorithm and language effects. Implications of these computational results for experimental and modeling approaches to language acquisition are discussed.