Cytogenetic aberrations have long been recognized as the most important prognostic variable in acute myeloid leukemia (AML) and are now a major stratification tool for post-remission therapy. Cytogenetics-based stratification improves survival. Patients with AML and normal cytogenetics, the largest single subgroup, have had a very heterogeneous outcome with standard chemotherapy in multiple clinical trials. Hence it is difficult to recommend a "one size fits all" kind of treatment for this heterogeneous population of AML patients. New emerging data from preclinical, retrospective, and large, randomized controlled studies indicate that in addition to cytogenetic abnormalities, many other molecular aberrations are operative in the response to treatment as well as in the risk of relapse. Such molecular markers are being tested for developing targeted therapies and may help in improved stratification of patients in the selection of post-remission therapy. Emerging evidence reveals that at the submicroscopic level, AML with normal cytogenetics may carry poor prognostic genetic lesions or "molecular signatures" as is the case with FLT3 mutations and overexpression of BAALC, ERG or MN1, or may have aberrations that predict better risk as is the case with isolated NPM1 or CEBPA mutations. Later studies have tried to explore the interaction of various prognostically important genes in this group of AML patients. The utility of the evolving data for bedside management of such patients is expected to improve with the wider application of modern tools, using the proposed clinical outcome models, and probably by development of a risk-scoring system based on the relative risk associated with each molecular aberration. The goals include identifying those patients most likely to benefit from upfront allogeneic HSCT and sparing good-prognosis patients from unnecessary transplant-related morbidity. The following is an outline of the most common molecular changes, their impact on the outcome of AML patients with normal cytogenetics and challenges in their wide scale application in risk stratification.