The cognitive radio system is proposed as an optimal way to improve the frequency underutilization. Spectrum sensing is the first and the essential function in this approach. A cognitive user must sense his environment to detect the unused channels, and then he can use the free channel without causing any interference to the primary user. In this article, an innovative technique is proposed for spectrum sensing based on principal component analysis and neural networks in frequency domain. The designed blocks are described using VHSIC Hardware Description Language (VHDL). The suggested application consists of extracting features from the captured signals by PCA; the classification is done by a Multi-Layer Perceptron (MLP). Neural network training part and principal components are done on MATLAB environment; while the hardware implementations are created on an FPGA DE2-70board.