We combine the New Immigrant Survey (NIS), which contains information on US legal immigrants, with the American Community Survey (ACS), which contains information on all immigrants to the U.S., legal and illegal ones. Using econometric methodology proposed by Lancaster and IOmbens (1996) we compute the probability for each observation in the ACS data to refer to an illegal immigrant, conditional on observed characteristics.The results for illegal versus legal immigrants are novel, since no other work has quantified the characteristics of illegal immigrants from a random sample. We find that, compared to legal immigrants, illegal immigrants are more likely to be less educated, males, and married with spouse not present. These results are heterogeneous across education categories, country of origin (Mexico) and whether professional occupations are included or not in the analysis. Forecasts for the distribution of certain legal and illegal characteristics match those available from other sources, such as aggregate imputations by the Department of Homeland Security for illegal immigrants.