Evaluation of body composition is considered central to any individual health assessment to identify those at risk for disease as well as for monitoring the efficacy or effectiveness of pharmacological or lifestyle interventions. It is well established that obesity (defined by BMI) is associated with almost all chronic conditions, and there are a range of common techniques (e.g., CT, MRI, pQCT, DXA, bioelectrical impedance (BIA) or spectroscopy (BIS)] that can provide more informative and accurate quantification of body fat composition. In contrast, the important role of skeletal muscle to health and disease has been largely underappreciated until more recently, when sarcopenia (defined as low muscle mass, strength and impaired function) was formally recognised as a disease with an ICD-10-CM code. There is now substantial evidence showing that low muscle mass is a significant risk factor many common chronic conditions, and thus there has been significant interest in its assessment in both research and clinical settings. However, accurately measuring ‘skeletal muscle mass’ can be challenging. MRI and (p)QCT can directly assess ‘muscle mass’ as well as the amount of fat infiltration within muscle (also called myosteatosis), which has been associated with a range of adverse health outcomes (e.g., reduced muscle strength, impaired function, insulin resistance, type 2 diabetes, increased inflammation, reduced bone density and increased fracture risk). Other common tools such as DXA and BIA/BIS only assess related components of muscle such as lean soft tissue mass (LSTM) or fat-free mass (FFM) which includes muscle and other body components (e.g., bone, water, organs), and thus provide estimates of ‘muscle mass’. This presentation will provide an overview of the importance of skeletal muscle to health and disease, review current techniques that can be used quantify muscle mass (and related components), including emerging techniques (e.g., the D3-creatine dilution method), new technologies (e.g., 3D body scanning, advanced ultrasound imaging, digital/wearable technology such as smart phone/watch applications) and whether prediction equations based on anthropometric and/or biochemical data are useful for estimating muscle mass when more advanced techniques are not available.