DNA methylation mediates imprinted gene expression by passing an epigenomic state across generations and differentially marking specific regulatory regions on maternal and paternal alleles. Imprinting has been tied to the evolution of the placenta in mammals and defects of imprinting have been associated with human diseases. Although recent advances in genome sequencing have revolutionized the study of DNA methylation, existing methylome data remain largely untapped in the study of imprinting. We present a statistical model to describe allele-specific methylation (ASM) in data from high-throughput short-read bisulfite sequencing. Simulation results indicate technical specifications of existing methylome data, such as read length and coverage, are sufficient for full-genome ASM profiling based on our model. We used our model to analyze methylomes for a diverse set of human cell types, including cultured and uncultured differentiated cells, embryonic stem cells and induced pluripotent stem cells. Regions of ASM identified most consistently across methylomes are tightly connected with known imprinted genes and precisely delineate the boundaries of several known imprinting control regions. Predicted regions of ASM common to multiple cell types frequently mark noncoding RNA promoters and represent promising starting points for targeted validation. More generally, our model provides the analytical complement to cutting-edge experimental technologies for surveying ASM in specific cell types and across species.