The aim of this study was to evaluate the efficacy of helical CT using a combination of CT-attenuation values and visual assessment of stone density as well as discriminant linear analysis to predict the chemical composition of urinary calculi. One hundred human urinary calculi were obtained from a stone-analysis laboratory and placed in 20 excised pig kidneys. They were scanned at 80, 120 and 140 kV with 3-mm collimation. Average, highest and lowest CT-attenuation values and CT variability were recorded. The internal calculus structure was assessed using a wide window setting, and visual assessment of stone density was recorded. A stepwise discriminant linear analysis was performed. The following three variables were discriminant: highest CT-attenuation value, visual density, and highest CT-attenuation value/area ratio, all at 80 kV. The probability of correctly classifying stone composition with these three variables was 0.64, ranging from 0.54 for mixed calculi to 0.69 for pure calculi. The probabilities of correctly classifying calculus composition were: 0.91 for calcium oxalate monohydrate and brushite, 0.89 for cystine, 0.85 for uric acid, 0.11 for calcium oxalate dihydrate, 0.10 for hydroxyapatite, and 0.07 for struvite calculi. When the first two ranks of highest probability for the accurate classification of each calculus type were taken into account, 81% of the calculi were correctly classified. Assessment at 80 kV of the highest CT-attenuation value, visual density and the highest CT-attenuation value/area ratio accurately predicts the chemical composition of 64–81% of urinary calculi. When the first two ranks of highest probability for the accurate classification of each calculus type were taken into account, all cystine, calcium oxalate monohydrate and brushite calculi were correctly classified.