The prevalence and burden of cardiovascular disease (CVD) is high, and it remains the leading cause of death worldwide. Unfortunately, many individuals who are at high risk for CVD are not recognized and/or treated. Therefore, programs are available to ensure individuals at risk for CVD are identified through appropriate risk classification and offered optimal preventative interventions. The use of algorithms to determine a global risk score may help to achieve these goals. Such global risk-scoring algorithms takes into account the synergistic effects between individual risk factors, placing increases in individual risk factors into context relative to the overall disease, allowing for a continuum of disease risk to be expressed, and identifying patients most likely to derive benefit from an intervention. The predictive value of risk scoring such as using the Framingham equation is reasonable, analogous to cervical screening, with area under the receiver operated characteristic curve a little over 70%. However, limitations do exist, and as they are identified adjustments can be made to the global risk-scoring algorithms. Limitations include patient-specific issues, such as variations in lifetime risk level, ethnicity or socio-economic strata, and algorithm-specific issues, such as discrepancies between different algorithms arising from varying risk factors evaluated. The use of currently developed algorithms is low in general practice, in part, because of the belief that the assessment may oversimplify the risk and/or lead to medication overuse. Additional hindrances to the use of risk scoring include government or local health policy, patient compliance issues and lack of time. A thorough, easy-to-use, and standardized tool for risk estimation would allow for improvements in the primary prevention of CVD.