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In many Arab countries' public administrations, Arabic personal names are written with Latin alphabet, generally, in various ways by different writers. This has led to many problems when it comes to connecting these administrations. The aim of this study was to propose two new approaches for the pairwise matching of Arabic personal names. The first approach is based on string alignment and phonetic transcription. Appropriate scoring functions were defined to catch similarity between Arabic personal names. In the second approach, we use machine learning techniques to derive a suitable model for this problem. Precisely, we suggest using a Multi-Layer Perceptron (MLP) architecture and experiment with different configurations. Performances of the new models compare well with the best-performing similarity measures (Jaro, Jaro-Winkler, Double Metaphone and Edit Distance) in terms of precision, recall and F1. Even though the work was carried out for the (Algeria/French Alphabet) case, it can be adapted to any other (country/script) case, like (Egypt/English).