In this paper, we present a hybrid approach to align single words, compound words and idiomatic expressions from bilingual parallel corpora. The objective is to develop, improve and maintain automatically translation lexicons. This approach combines linguistic and statistical information in order to improve word alignment results. The linguistic improvements taken into account refer to the use of an existing bilingual lexicon, named entities recognition, grammatical tags matching and detection of syntactic dependency relations between words. Statistical information refer to the number of occurrences of repeated words, their positions in the parallel corpus and their lengths in terms of number of characters. Single-word alignment uses an existing bilingual lexicon, named entities and cognates detection and grammatical tags matching. Compound-word alignment consists in establishing correspondences between the compound words of the source sentence and the compound words of the target sentences. A syntactic analysis is applied on the source and target sentences in order to extract dependency relations between words and to recognize compound words. Idiomatic expressions alignment starts with a monolingual term extraction for each of the source and target languages, which provides a list of sequences of repeated words and a list of potential translations. These sequences are represented with vectors which indicate their numbers of occurrences and the numbers of segments in which they appear. Then, the translation relation between the source and target expressions are evaluated with a distance metric. The single and compound word aligners have been evaluated on a subset of 1103 sentences in English and French of the JOC (Official Journal of the European Community) corpus. The obtained results showed that these aligners generate a translation lexicon with 90 % of precision for single words and 84 % of precision for compound words. We evaluated the idiomatic expressions aligner on a subset of the Canadian Parliament Hansard corpus and we obtained a precision of 81%.