Objectives: Sarcopenia is the loss of muscle mass and function seen with increasing age. Central to making the diagnosis of sarcopenia is the assessment of appendicular skeletal muscle mass (ASM). The objective of this study was to develop and validate novel anthropometric prediction equations (PEs) for ASM that would be useful in primary or aged care. Design: PEs were developed using best subset regression analysis. Three best performing PEs (PE1, PE2, PE3) were selected and validated using the Bland-Altman and Sheiner & Beal methods. Setting: Community dwelling adults in South Australia. Participants: 188 healthy subjects were involved in the development study. 2275 older(age > 50years) subjects were involved in the validation study. Measurements: ASM was assessed using dual x-ray abosrptiometry (DEXA). Weight and height was measured and body mass index (BMI) estimated. Results: A strong correlation between PE derived ASM and the DEXA derived ASM was seen for the three selected PEs. PE3: ASM= 10.047427 + 0.353307(weight) - 0.621112(BMI) - 0.022741(age) + 5.096201(if male) performed the best. PE3 over-estimated (P<0.001) ASM by 0.36 kg (95% CI 0.28-0.44 Kg) and the adjusted R2 was 0.869. The 95% limit of agreement was between -3.5 and 4.35 kg and the standard error of the estimate was 1.95. The root mean square error was 1.91(95% CI 1.80-2.01). PE3 also performed the best across the various age (50-65, 65-<80, 80+ years) and weight (BMI <18.5, 18.5-24.9, 25-29.9, >30 kg/m2) groups. Conclusions: A new anthropometric PE for ASM has been developed for use in primary or aged care but is specific to Caucasian population groups.