Objectives: AmpC-β-lactamase production is an under-recognized antibiotic resistance mechanism that renders Gram-negative bacteria resistant to common β-lactam antibiotics, similar to the well-known ESBLs. For infection control purposes, it is important to be able to discriminate between plasmid-mediated AmpC (pAmpC) production and chromosomal-mediated AmpC (cAmpC) hyperproduction in Gram-negative bacteria as pAmpC requires isolation precautions to minimize the risk of horizontal gene transmission. Detecting pAmpC in Escherichia coli is challenging, as both pAmpC production and cAmpC hyperproduction may lead to third-generation cephalosporin resistance. Methods: We tested a collection of E. coli strains suspected to produce AmpC. Elaborate susceptibility testing for third-generation cephalosporins, WGS and machine learning were used to develop an algorithm to determine ampC genotypes in E. coli. WGS was applied to detect pampC genes, cAmpC hyperproducers and STs. Results: In total, 172 E. coli strains (n=75 ST) were divided into a training set and two validation sets. Ninety strains were pampC positive, the predominant gene being blaCMY-2 (86.7%), followed by blaDHA-1 (7.8%), and 59 strains were cAmpC hyperproducers. The algorithm used a cefotaxime MIC value above 6 mg/L to identify pampC-positive E. coli and an MIC value of 0.5 mg/L to discriminate between cAmpC-hyperproducing and non-cAmpC-hyperproducing E. coli strains. Accuracy was 0.88 (95% CI=0.79-0.94) on the training set, 0.79 (95% CI=0.64-0.89) on validation set 1 and 0.85 (95% CI=0.71-0.94) on validation set 2. Conclusions: This approach resulted in a pragmatic algorithm for differentiating ampC genotypes in E. coli based on phenotypic susceptibility testing.