Satellite image time-series (SITS) data are a set of satellite images acquired from the same geographic area over a period of time. SITS data, due to their ability to capture the dynamic spectral behavior of the crop during their growing season, have been increasingly used for accurate crop mapping. The time-series obtained from the multispectral or hyperspectral sensors can be considered as multivariate time-series. The classification of these type of time-series data is a challenging task. This is mainly due to the fact that these data can be considered as a four-dimensional data, so the available classification algorithms cannot be used for their classification. To address this issue, in this paper by using the Multiple Kernel Learning (MKL) algorithms, a novel method for classification of multivariate time-series data is proposed. MKL algorithms are a group of kernel learning algorithms that make it possible to use a combination of kernels instead of a single one for kernel-based learning algorithm such as classification.  In the proposed method, initially one kernel is constructed from data of each time of the time-series and then by using the MKL algorithms, these kernels are optimally combined into a composite kernel. The obtained composite kernel, once constructed, can be used to classify the time-series data by using all the kernel-based classification algorithms.  In order to evaluate the proposed method, two time-series data were used. Both these time-series consisted of 10 different RapidEye imageries, acquired over an agricultural area in Manitoba, Canada. Both these time-series contained the main crop types of the region such as wheat, corn, canola, and soybean. In order to evaluate the effects of different MKL algorithms in the framework of the proposed method, in addition to the common MKL algorithms, the Generalized Multiple Kernel Learning algorithm (GMKL) was adopted as the MKL algorithms in the proposed Method. The GMKL is one of the most recent MKL algorithms proposed in machine learning literature, which has not been evaluated for the time-series data analyses. As a benchmark for comparison with a single kernel method, stacking method, in which the data acquired at different times are stacked into a single data cube, was used. The composite kernel obtained from the proposed algorithm with adopting different MKL algorithms and the kernel constructed from the data cube obtained from the stacking of the data were used to train of a Support vector machine algorithm. The obtained classification accuracies of the SVM showed a dramatic increase (at least 4.9% increase in the overall accuracy of the classification) in the case of using the kernel obtained from the proposed method in comparison with the case of using the kernel obtained from the stacked data cube. Moreover, the GMKL algorithm showed a higher performance in comparison to other MKL algorithms in the context of the proposed method for multivariate time-series classification.  In addition, the proposed method showed better performance in the presence of cloud and cloud shadows in the data. This is because the MKL algorithms can reduce the negative effects of the cloud contaminated images within the time-series.