In this paper, we propose a new global and fast Multilayer Perceptron Neural Network (MLP-NN) which can be used to forecast the automotive price. Nowadays, the gradient-based techniques, such as back propagation, are widely used for training neural networks. These techniques have local convergence results and, therefore, can perform poorly even on simple problems when forecasting is out of sample. On the other hand, the global search algorithms, like Tabu Search (TS), suffer from low rate convergence. Motivated by these facts, a new global and fast hybrid algorithm for training MLP-NN is provided. In our new framework, a hybridization of an extended version of TS with some local techniques is constructed in order to train the connected weights of the network. The extended version of TS in the proposed scheme consists of a simple TS together with the intensification and diversification search methods, and the local search methods are based on a direct strategy of Nelder-Mead (NM) or Levenberg-Marquardt (LM) techniques. This hybridization leads us to have a global and fast trained network in order to use in some forecasting problems. To show the efficiency and effectiveness of our new proposed network, we apply our new scheme for forecasting the automotive price in Iran Khodro Company which is the biggest car manufacturer in Iran. The results are promising compared to the cases when we apply the TS and some other forecasting techniques individually. We also compare the results with the case when we employ the gradient-based optimization techniques such as LM, and global search methods such as Genetic Algorithm (GA) and hybrid of MLP-NN with GA.