Turbulence modeling is among the most important challenges of researchers. Over the past decades, different models have been proposed to explain the behavior of turbulent flows. An important feature of turbulent flows is the chaotic characteristics of the flow field. Such a feature has led researchers to adopt statistical approaches in tackling turbulence modeling challenge. The averaging method, in which the instantaneous quantities are decomposed into the mean and fluctuating components, is a well-known approach in turbulence modeling. As a result of this scheme, new unknowns are introduced into the governing equations that are subject to further modeling. Two of the most common sub-models in this approach are the two-equation model, namely the k-? model, and the algebraic stress model. The former has the advantage of computational economy and relative stability of the numerical solution procedure, but suffers from inaccuracy in predicting the normal stresses. The latter model is able to deliver a better estimation of normal stresses, but weaker stability and inaccurate shear stress predictions. The present work aims to combine the strong features of both models, while avoiding their flaws. A new blend model for the turbulent stresses is introduced. The model adopts two well known turbulence models, the k-? model and the explicit algebraic stress model of Speziale (SSG), and blends them. The model may be implemented in a binary method, in which the k-? model for shear stresses and the explicit algebraic stress model for normal stresses is used, or a fuzzy method which is a combination of both the aforementioned models. The implementation is done by writing the momentum equations based on the k-? model and adding the extra terms due to the blend model as momentum sources. The new model benefits from favorable features of k-? and algebraic stress models, while avoiding their deficiencies in terms of accuracy and computational economy. Results of an in-house CFD code based on the blend model for two benchmark cases, namely a backward-facing step and a shallow cavity, show that both the proposed binary and fuzzy models are capable of correctly predicting the turbulent normal and shear stresses. The simulation results for both cases reveal that the blend model performs better than the k-? model and the explicit algebraic model in terms of the accuracy. The results of the fuzzy model in both cases are superior to those of k-? , SSG, and the binary model. The CPU time consumed for the blend model (binary or fuzzy) is less than the SSG model, but more than the k-? model. A more complex geometry in 3D has been considered to verify the ability of the proposed models in complex flows. An industrial case, namely the Stairmand cyclone, has been simulated using k-?, SSG, and the proposed hybrid models. Results show that the proposed models are capable of correctly predicting the tangential velocity, but yielding wrong predictions for the axial velocity. Overall, the blend models proved to be able to predict the flow field quantities on average 32.4 percent and at worst 8.34 percent improvement compared to other models. Keywords: turbulence models , hybrid model , algebraic stress , two equation , backward-facing step, shallow cavity , stairmand cyclone