The process of aging causes a wide variety of physiological changes that can manifest in the form of differing body composition phenotypes. A systematic approach to body composition classification and the subsequent selection of appropriate interventions is needed for community-based health care and fitness specialists. The primary purpose of this investigation was to determine body composition classification using field-based testing measurements in healthy elderly men and women. The use of isoperformance curves is presented as a method for this determination. Baseline values from 107 healthy Caucasian men and women over the age of 65 years old who participated in a separate longitudinal study were used for this investigation. Age, height, weight, body mass index (BMI), and handgrip strength were recorded on an individual basis. Relative skeletal muscle index (RSMI) and body fat percentage (FAT%) were determined by dual-energy X-ray absorptiometry (DXA) for each participant. Sarcopenia cut-off values for RSMI of 7.26 kg·m-2 for men and 5.45 kg·m-2 for women and elderly obesity cut-off values for FAT% of 27% for men and 38% for women were used. Individuals above the RSMI cut-off and below the FAT% cut-off were classified in the normal phenotype category, while individuals below the RSMI cut-off and above the FAT% cut-off were classified in the sarcopenic-obese phenotype category. The relationship between age and BMI, handgrip strength, RSMI, and FAT% was characterized using linear regression. Prevalence values for body composition phenotypes from actual DXA-based criteria and predicted RSMI and FAT% were evaluated. Using the DXA criterion values for RSMI and FAT%, 34 individuals (32% of the sample) were classified as normal, 50 individuals (47% of the sample) were classified as obese, 10 individuals (9% of the sample) were classified as sarcopenic, and 13 individuals were classified as sarcopenic obese. Prediction equations for RSMI and FAT% from BMI and handgrip strength values were developed using multiple regression analysis. The prediction equations were validated using double cross-validation. The final regression equation developed to predict FAT% from BMI and handgrip strength resulted in a strong relationship (adjusted R2=0.741) to DXA values with a low standard error of the estimate (SEE=3.9937%). The final regression equation developed to predict RSMI from the field-based testing measures also resulted in a strong relationship (adjusted R2=0.841) to DXA values with a low standard error of the estimate (SEE=0.5437 kg·m-2). Using the prediction values for FAT% and RSMI, 30 individuals (28% of the sample) were classified as normal, 58 individuals (54% of the sample) were classified as obese, 17 individuals (16% of the sample) were classified as sarcopenic, and 2 individuals (2% of the sample) were classified as sarcopenic obese. Subsequently, isoperformance curves were used to aid in the classification and evaluation of sarcopenia, obesity, and sarcopenic obesity in elderly individuals by graphically representing the relationship between BMI and handgrip strength with the aforementioned clinical phenotype classification criteria. The final goal of this investigation was to produce easily understood charts that can be used by personal trainers, nutrition specialists, and/or health professionals. The charts could be used in the classification of individuals into these phenotype categories in an inexpensive and non-invasive manner. Future research should be undertaken that enhances the current findings by increasing the sample size and developing tailored interventions for each body composition category.