Time-domain nuclear magnetic resonance (TD-NMR) was explored as a rapid method for simultaneous assessment of the quality parameters in commercial diesel samples (B5 diesel-biodiesel blend). A principal component analysis (PCA) obtained with the relaxation decay curves revealed tight and well-separated clusters, allowing discrimination of the diesel samples according to the sulfur content: 10 (S10), 500 (S500), and 1800 (S1800) mg kg(-1). Classification models based on the soft independent modeling of class analogy (SIMCA) showed a good discrimination power with a percentage of correct classification ranging from 90% (for S500 diesel samples) to 100% (for S10 and S1800 diesel samples). Partial least-squares regression (PLSR) was used to estimate the cetane index, density, flash point, and temperature achieved during distillation to obtain 50% of the distilled (T50) physicochemical parameters in the commercial diesel samples. The best PLSR models were obtained with two latent variables, providing a standard error of prediction (RMSEP) of 0.60, 2.37 kg m(-3), 3.24, and 2.20 degrees C for the cetane index, density, flash point, and T50, respectively, which represents the accuracy of the models. The results support the application of TD-NMR to evaluate the quality of B5 diesel, providing a simple, rapid, and nondestructive method for the petrofuel industry.