This study describes the performance results on testing MatLab applications using the parallel computing and the distributed computing toolboxes under different platforms with different hardware and operating systems. Each trial was executed keeping the hardware fixed and changing the operating system to obtain unbiased results. To standardize the benchmarking test, Fast Fourier Transform (FFT), discrete cosine transform (DCT), edge detection and matrix multiplication algorithms were executed. The results show that the leveraging of multicore platforms can speed up considerably the processing of images through the use of parallel computing tools in MatLab. Two different system hardware platforms (systems 1 and 2) were used in a series of experiments. Four rounds of experiments were performed benchmarking the FFT algorithm using the parallel tool box, by changing system platform, number of workers, image size and number of images. The results of the ANOVA test suggest that although there is no statistical significance on the factor represented by the operating system (OS) on system 1, the OS plays a significant roll on system 2. Moreover, on both systems there is statistical significance on the factors represented by the number of workers utilized and the number of images processed, yielding more than a 500% performance increase by using 8 MatLab workers on a dual quad-core machine.