Escherichia coli can be used to help identify sources of fecal contamination in the environment. Escherichia coli genotypic fecal libraries and pattern-matching algorithms were assessed for their effectiveness in correctly identifying sources. Fecal samples (n = 172) were collected from various sources from three agricultural landscapes in Canada. Escherichia coli isolates were fingerprinted using BOX- and enterobacterial repetitive intergenic consensus (ERIC) - polymerase chain reaction primers, revealing 769 and 1 057 distinct genotypes, respectively, for the 9 047 isolates collected in 2004 in Ontario. The average rate of correct classification (ARCC) was comparable for BOX- (48%) and ERIC-based (62%) libraries and between libraries with clones removed per sample (55%) and clones removed per unit (54%). ARCC increased with fewer classification units (from 44% to 65%). ARCC for k-nearest neighbour (64%) and maximum similarity (60%) algorithms were comparable, but maximum similarity had better sensitivity and specificity than k-nearest neighbour. Geographical and temporal shifts in community composition resulted in loss of accuracy. Several ERIC genotypes (n = 112) were common between sources and were removed from the library, improving ARCC (77%). The latter library proved to be more accurate, but its accuracy with respect to sourcing environmental isolates remains to be tested.