Protein identification using Mass Spectrometry (MS) is essential in the study of proteomics. Two popular techniques are used in the identification: Tandem Mass Spectrometry (MS/MS) and Peptide Mass Fingerprinting (PMF), which is considered in this work. PMF is widely used in the proteomics field. It is faster and more economic when compared to MS/MS. This work focuses on the development of a computational tool for protein identification using PMF data. The main objective for any PMF tool is to identify the correct protein (if it exists) by searching a peak list, produced by MS, against a protein database. However, one of the great challenges to these tools is related to the size of the databases that result in many random matches. In fact, the main difference between these tools is the scoring method which is responsible of minimizing these random matches. Therefore, a review of PMF tools and their scoring methods is presented and discussed. There are many tools on the Internet (both commercial or academic) for PMF protein identification using public databases. These tools do not offer a locally installable version, and do not allow the use of in-house databases, a feature that is of great importance to biologists who work on non-model systems. In contrast, the tool developed in this work is free, can be installed locally, and can be used with both public and local databases. Additionally, it supports different sorts of protein modifications and contaminants suppression, features that are not available by some of the existing tools. A new scoring method is proposed and incorporated in the proposed tool. The proposed tool is compared with two of the most popular software packages (commercial and academic), showing a good accuracy and being very competitive with the most popular and robust commercial software (Mascot). The developed prototype is platform-independent and is very easy to install. To allow users to work and interact with the system in an easy-to-use environment, a friendly graphical user interface is developed to allow them to manage their files very efficiently. In addition, it can work with single or multiple query files to support different work scales. The features this new tool offers make it an important assist to the biological laboratories concerning the PMF task.