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Lipid profile usually includes levels of total cholesterol (TCH), low density lipoprotein (LDL), high density lipoprotein (HDL) and triglycerides (TG), all of which require a blood test. Using advances in machine learning and a relationship between lipid profile and obesity, a model that predicts lipid profile without using any laboratory results can be developed and used in clinical diagnosis. The causal relationship between lipid profile and obesity is well known—TCH, LDL and TG show an increase, while HDL is decreased in obese persons. In this paper we are using artificial neural networks (ANN) to estimate the lipid profile values using non-lab electronic health record data and some measures of obesity. The ANN inputs are gender, age, systolic and diastolic blood pressures, and a single or a combination of multiple obesity parameters, which include body mass index, saggital abdominal diameter to height ratio, waist to height ratio and body fat percentage. Study shows that the presented solution is suitable for prediction of TCH (with accuracy 81.89%), LDL (with accuracy 79.29%) and HDL (with accuracy 81.23%), while not suitable for TG prediction (with accuracy 44.48%). © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.