The Europarl corpus, a parallel text corpus of 11 languages from the European Parliament, has been widely used in the NLP community for statistical machine translation (SMT). This paper describes the acquisition of the corpus and its application in training SMT systems for 110 language pairs. The corpus, consisting of about 30 million words in each of the 11 official languages of the European Union, was collected from the European Parliament's website. The collection process involved crawling the website, extracting and aligning documents, sentence splitting, tokenization, and sentence alignment. The corpus was then used to train SMT systems, which demonstrated the different challenges for SMT across language pairs. The paper also discusses the results of these systems, showing that some language pairs are easier to translate than others. The study highlights the importance of parallel corpora in SMT and the challenges of translating between languages with different morphological structures. The paper concludes that while the availability of resources like the Europarl corpus has made SMT an exciting field, there is still much to be done in terms of improving translation quality and handling a wider range of language pairs.The Europarl corpus, a parallel text corpus of 11 languages from the European Parliament, has been widely used in the NLP community for statistical machine translation (SMT). This paper describes the acquisition of the corpus and its application in training SMT systems for 110 language pairs. The corpus, consisting of about 30 million words in each of the 11 official languages of the European Union, was collected from the European Parliament's website. The collection process involved crawling the website, extracting and aligning documents, sentence splitting, tokenization, and sentence alignment. The corpus was then used to train SMT systems, which demonstrated the different challenges for SMT across language pairs. The paper also discusses the results of these systems, showing that some language pairs are easier to translate than others. The study highlights the importance of parallel corpora in SMT and the challenges of translating between languages with different morphological structures. The paper concludes that while the availability of resources like the Europarl corpus has made SMT an exciting field, there is still much to be done in terms of improving translation quality and handling a wider range of language pairs.