The paper presents how the current leakage encountered in capacitive analog memories affects the learning process of hardware implemented Kohonen neural networks (KNN). MOS transistor leakage currents, which strongly depend on temperature, increase the network quantization error. This effect can be minimized in several ways discussed in the paper. One of them relies on increasing holding time of the memory. The presented results include simulations in Matlab and HSpice environments, as well as measurements of a prototyped KNN realized in a 0.18um CMOS process.