Caret train with glmnet give me Error "arguments imply differing number of rows"

Hello,

I'm training a set of data with Caret package using an elastic net (glmnet).
Most of the time train works ok, but when the data set grows in size I get
the following error:
Error en { :
 task 1 failed - "arguments imply differing number of rows: 9, 10"

and several warnings like this one:
1: In eval(expr, envir, enclos) :
 model fit failed for Resample01

My call to train function is like this:
fit <- train(TrainingPreCols, TrainingFrame[,PCol], method="glmnet",
preProcess = c("center","scale"))

When TrainingPreCols is 17420 obs. of 27 variables, the function works ok.
But with a size of 47000 obs of 27 variables I get the former error.

?Could be the amount of data the cause of this error?

Any help is appreciated,
 Ferran

P.D.:
This is my sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=Spanish_Spain.1252  LC_CTYPE=Spanish_Spain.1252
LC_MONETARY=Spanish_Spain.1252
[4] LC_NUMERIC=C                   LC_TIME=Spanish_Spain.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] glmnet_1.9-3    Matrix_1.0-12   doSNOW_1.0.7    iterators_1.0.6
snowfall_1.84-4
[6] snow_0.3-12     caret_5.16-04   reshape2_1.2.2  plyr_1.8
lattice_0.20-6
[11] cluster_1.14.4  foreach_1.4.1

loaded via a namespace (and not attached):
[1] codetools_0.2-8 grid_2.15.0     stringr_0.6.2   tools_2.15.0

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