Self-monitoring of blood glucose (SMBG) devices are portable systems that allow measuring glucose concentration in a small drop of blood obtained via finger-prick. SMBG measurements are key in type 1 diabetes (T1D) management, e.g. for tuning insulin dosing. A reliable model of SMBG accuracy would be important in several applications, e.g. in in silico design and optimization of insulin therapy. In the literature, the most used model to describe SMBG error is the Gaussian distribution, which however is simplistic to properly account for the observed variability. Here, a methodology to derive a stochastic model of SMBG accuracy is presented. The method consists in dividing the glucose range into zones in which absolute/relative error presents constant standard deviation (SD) and, then, fitting by maximum-likelihood a skew-normal distribution model to absolute/relative error distribution in each zone. The method was tested on a database of SMBG measurements collected by the One Touch Ultra 2 (Lifescan Inc., Milpitas, CA). In particular, two zones were identified: zone 1 (BG≤75 mg/dl) with constant-SD absolute error and zone 2 (BG>75mg/dl) with constant-SD relative error. Mean and SD of the identified skew-normal distributions are, respectively, 2.03 and 6.51 in zone 1, 4.78% and 10.09% in zone 2. Visual predictive check validation showed that the derived two-zone model accurately reproduces SMBG measurement error distribution, performing significantly better than the single-zone Gaussian model used previously in the literature. This stochastic model allows a more realistic SMBG scenario for in silico design and optimization of T1D insulin therapy.