Hiring the right person for the right job is always a challenging task in software development landscapes. To bridge this gap, software_rms start using psychometric instruments for investigating the personality types of software practitioners. In our previous research, we have developed an MBTI-like instrument to reveal the personality types ofsoftware practitioners. This study aims to develop a personality-based team recommender mechanism to improve the e_ectiveness of software teams. The mechanism is based on predicting the possible patterns of teams using a machine-based classi_er. The classi_er is trained with em-pirical data (e.g. personality types, job roles), which was collected from52 software practitioners working on _ve different software teams. 12software practitioners were selected for the testing process who were recommended by the classi_er to work for these teams. The preliminary results suggest that a personality-based team recommender system mayprovide an effective approach as compared with ad-hoc methods of teamformation in software development organizations. Ultimately, the overallperformance of the proposed classi_er was 83.3%. These _ndings seemacceptable especially for tasks of suggestion where individuals might beable to _t in more than one team.