Context : The statistical analysis of large sample of strong lensing events can be a powerful tool to extract astrophysical and/or cosmological valuable information .
However , the number of such events is still relatively low , mostly because of the lengthily observational validation process on individual events .

Aims : In this work we propose a new methodology with a statistical selection approach in order to increase by a factor of \sim 5 the number of such events .
Although the methodology can be applied to address several selection problems , it has particular benefits in the case of the identification of strongly lensed galaxies : objectivity , minimal initial constrains in the main parameter space , preservation of the statistical properties .

Methods : The proposed methodology is based on the Bhattacharyya distance as a measure of the similarity between probability distributions of properties of two different cross-matched galaxies .
The particular implementation for the aim of this work is called SHALOS and it combines the information of four different properties of the pair of galaxies : angular separation , luminosity percentile , redshift and optical/sub-mm flux density ratio .

Results : The SHALOS method provided a ranked list of strongly lensed galaxies .
The number of candidates for the final associated probability , P _ { tot } > 0.7 , is 447 with an estimated mean amplification factor of 3.12 for an halo with a typical cluster mass .
Additional statistical properties of the SHALOS candidates , as the correlation function or the source number counts , are in agreement with previous results indicating the statistical lensing nature of the selected sample .

Conclusions :